text:
|Publication number
|Publication type
|Application number
|Publication date
|Jun 25, 2002
|Filing date
|Feb 12, 1999
|Priority date
|Nov 18, 1998
|Fee status
|Publication number
|Christopher John Burges
|Original Assignee
|Lucent Technologies Inc.
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (8), Non-Patent Citations (2), Referenced by (73), Classifications (11), Legal Events (5)
|External Links: USPTO, USPTO Assignment, Espacenet
This application claims the benefit of the Nov. 18, 1998, filing date of Provisional Application Serial No. 60/108,972 entitled "Discriminative Gaussian Mixture Models For Speaker Identification".
This invention relates generally to methods and apparatus for use in performing speaker identification.
In systems that provide for identification of a speaker, a general technique is to score the speaker's enunciation of a test phrase against each one of a number of individual Gaussian mixture models (GMM) and to select, or identify, the speaker as that person associated with the individual GMM, or set of GMMs, achieving the best score above a certain threshold using, e.g., a maximum likelihood technique. Typically, these systems generate individual GMMs by independently training, a priori, on small (e.g., 30 milli-second (ms.)) speech samples of training phrases spoken by the respective person.
Unfortunately, such systems do not perform well when attempting to discriminate the true speaker from people that merely sound like the true speaker. As such, in an attempt to improve discrimination these systems increase the number of GMMs to include "cohort" or "background" models, i.e., people that sound like the true speaker but are not (e.g., see Herbert Gish and Michael Schmidt, "Text-independent speaker identification," IEEE Signal Processing Magazine, pages 18-32, 1994).
Alternatively, for both the speech and speaker recognition problems, a different approach has recently been proposed which uses a discriminative cost finction (which measures the empirical risk) during training in place of the maximum likelihood estimation, giving significantly improved generalization performance (e.g., see, Biing-Hwang Juang, Wu Chou, and Chin-Hui Lee, "Minimum Classification Error Rate Methods for Speech Recognition," IEEE Transactions on Speech and Audio Processing, 5(3):257-265, 1997; and Chi-Shi Lui Chin-Hui Lee, Wu Chou, Biing-Hwang Juang, and Aaron E. Rosenberg, "A study on minimum error discriminative training for speaker recognition," Journal of the Acoustical Society of America, 97(1):637-648, 1995). However, here the underlying model (a set of hidden Markov models) is left unchanged, and in the speaker recognition case, only the small vocabulary case of isolated digits was considered.
In providing speaker identification systems such as described above, support vector machines (SVMs) have been used for the speaker identification task directly, by training one-versus-rest and one-versus-another classifiers on the preprocessed data (e.g., see M. Schmidt, "Identifying speaker with support vector networks," Interface '96 Proceedings, Sydney, 1996). However, in such SVM-based speaker identification systems, training and testing are both orders of magnitude slower than, and the resulting performance is similar to, that of competing systems (e.g., see also, National Institute for Standards and Technology, Speaker recognition workshop, Technical Report, Maritime Institute of Technology, Mar. 27-28, 1996).
Unfortunately, the above-described approaches to speaker-identification are not inherently discriminative, in that a given speaker's model(s) are trained only on that speaker's data, and effective discrimination relies to a large extent on finding effective score normalization and thresholding techniques. Therefore, I have developed an alternative approach that adds explicit discrimination to the GMM method. In particular, and in accordance with the invention, I have developed a way to perform speaker identification that uses a single Gaussian mixture model (GMM) for multiple speaker—referred to herein as a Discriminative Gaussian mixture model (DGMM).
In an illustrative embodiment of the invention, a DGMM comprises a single GMM that is used for all speakers. A likelihood sum of the GMM is factored into two parts, one of which depends only on the Gaussian mixture model, and the other of which is a discriminative term. The discriminative term allows for the use of a binary classifier, such as a support vector machine (SVM).
In another embodiment of the invention, a voice messaging system incorporates a DGMM. The voice messaging system comprises a private branch exchange (PBX) and a plurality of user terminals, e.g., telephones, personal computers, etc.
FIG. 1 is an illustrative flow chart embodying the principles of the invention; and
FIGS. 2-5 show illustrative block diagrams of DGMM voice mail systems embodying the principles of the invention.
Before describing an illustrative embodiment of the invention, a brief background is provided on the above-mentioned prior art speaker identification approach using individual, non-discriminative, GMMs. In the following description, the phrase "target speaker" means a speaker whose identity the respective system is supposed to determine (note, there may be non-target speakers in the training and test sets). Other than the inventive concept, it is assumed that the reader is familiar with mathematical notation used to generally represent kernel-based methods as known in the art. Components of vectors and matrices are labeled with Greek indices, vectors and matrices themselves are labeled with Roman indices.
Gaussian Mixture Models: The Standard Approach
In a typical GMM system, one (or several) GMMs are built for each speaker. The data is preprocessed into low dimensional feature vectors, typically using 20 ms frame sizes and 10 ms steps. The features used are often some or all of the following (as known in the art): vectors of cepstral, delta-cepstral, and delta-delta-cepstral coefficients, a scalar measure of energy, and a scalar measure of pitch.
A Gaussian mixture model is simply a weighted sum of Gaussian densities, where the positive (scalar) weights sum to unity (which results in the sum itself being a density). It is desired to model the posterior conditional probability P(Si|x1, . . . , xm), where Si=1, . . . , N
where here and below, P denotes probabilities, p the corresponding densities, and {tilde over (r)} is shorthand for the set of feature vectors x1, . . . , xm. Thus in order to find the target speaker who most likely generated a given set of test feature vectors {tilde over (x)}, the posterior probability P(Si|{tilde over (x)}) is maximized over the choice of speaker Si. If it is assumed that all speakers have equal priors (P(Si)=constant) then clearly this amounts to finding the maximum likelihood (p({tilde over (X)}|Si)) over all target speakers. This approach is sufficient for the "closed set" problem, where the test speech is guaranteed to have originated from a target speaker. In the harder, "open set" problem, the speech may or may not have been generated by a target speaker, and some thresholding algorithm is necessary to decide whether to reject or accept the message (i.e., whether to assert that it was generated by one of the target speakers). In the description that follows, the open set problem is considered and the assumption is made that the random variables x1, . . . , xm are independent, so that the density (p({tilde over (x)}|Si)) can be expanded as a product. The likelihood is then modeled as a Gaussian mixture:
where the sum in equation (2) is over a set of NC mutually exclusive and complete events ("clusters") Cj and where μij is the mean for the Gaussian distribution for speaker i's j'th cluster, Σij is the corresponding covariance matrix (recall that i, j here label the vectors and matrices themselves, not their components), and |Σij| its determinant (e.g., see A. A. Sveshnikov, Problems in probability theory, mathematical statistics and theory of random functions, Dover Publications, New York, 1978).
To train a GMM for a given speaker Si, one starts by specifying how many Gaussians to use (Nc). This is typically anywhere from 20 to 150. Some in the art choose 50 to roughly match the number of phonemes (e.g., see article by Gish and Schmidt, cited above). Then the training feature vectors are clustered into NC clusters, using, for example, the K means algorithm (e.g., see V. Fontaine, H. Leich, and J. Hennebert; "Influence of vector quantization on isolated word recognition," in M. J. J. Holt, C. F. N. Cowan, P. M Grant, and W. A. Sandham, editors, Signal Processing VII, Theories and Applications; Proceedings of EUSIPCO-94, Seventh European Signal Processing Conference, volume 1, pages 116-18, Lausanne, Switzerland, 1994, Eur. Assoc. Signal Process). The resulting means and covariances form the starting point for GMM training, for which a simple variant of the EM algorithm is used (e.g., see, A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society B, 39(1):1-22, 1977; and the article by Fontaine et al, cited above). In particular, for each cluster, and given the cluster memberships output by the K means algorithm, the mean and covariance matrices are recomputed:
In equations (4, 5), the xa are those feature vectors which are members of cluster Ci, where cluster membership is determined by maximum likelihood. Then the cluster memberships are recomputed using likelihoods computed using the new means and covariances. The maximum likelihood cluster for vector x is thus given by
It should be noted that this is really a maximum posterior computation, since the priors P(Cj) (which are estimated by counting cluster memberships) are used. This two-step process is then iterated until convergence (i.e. until cluster membership stabilizes).
In test phase, one computes the sum log likelihood
that a given test message (from which m feature vectors were extracted) was generated by a given speaker Si's GMM, and combined with a suitable normalization and thresholding scheme, either rejects the message (i.e., asserts that the message was not generated by a target speaker), or identifies the target and outputs a confidence value. Here p(xa|Si,Cj) is given by equation (3), and P(Cj|Si) is estimated by counting cluster memberships for speaker i's training data.
An intriguing property of the Gaussian distribution is that the dependence of the above sum on the test data can be described solely in terms of the mean and covariance of the set of test feature vectors (e.g., see above-cited Gish et al. article). Furthermore, the resulting expression can be derived by asking for the likelihoods of two characteristic statistics of the test data, namely its mean and covariance matrix. These observations lead to the idea of using the likelihoods of test mean and covariance as contributions to a weighted sum of likelihoods, resulting in the "modified Gaussian models" as described in the above-cited article of Gish et al. One can then easily incorporate the likelihood for the delta-cepstral covariance matrix into the sum (the delta-cepstral mean contains no new information).
The key remaining problem is how to normalize the scores from different GMMs so that they can be compared, in such a way that good discrimination results. The above-cited article by Gish et al., suggests a number of possibilities. First, if one has training messages corresponding to different sessions for a given speaker, at least one model is built for each, in the hope of capturing channel or session variations. One may further split a given training message into pieces and build a GMM for each piece, in order to model variation due to, for example, the occurrence of multiple speakers in the same message. A test message is also segmented, and each test message is attached to whichever model gives it the highest likelihood. The score is then normalized by dividing that likelihood by the highest occurring likelihood for that message for all models for all other speakers. Often, many "cohort" speakers (a target speaker's cohorts are a set of other speakers which are attributed high likelihood scores by that speaker's model) are added to the training set to help in the score normalization and discrimination. Even after normalization, there are many possible approaches to how to combine scores from different segments. Finally, given such an approach, it is useful also to estimate confidence values for the resulting score (e.g., see the above-cited article by Gish et al.).
Discriminative Gaussian Mixture Models
Unfortunately, the above approach is not inherently discriminative, in that a given speaker's model(s) are trained only on that speaker's data, and effective discrimination relies to a large extent on finding effective score normalization and thresholding techniques. Therefore, I have developed an alternative approach that adds explicit discrimination to the GMM method. In particular, and in accordance with the invention, I have developed a way to perform speaker identification that uses a single Gaussian mixture model (GMM) for multiple speakers—referred to herein as a Discriminative Gaussian mixture model (DGMM). Other than the inventive concept, the techniques described below encompass techniques found in standard speech preprocessing for use in speech recognition as well as speaker recognition.
The DGMM approach is based on two key ideas. First, create a single GMM for all target speakers. Thus this GMM is intended to model all speech generated by these speakers. (The building of a GMM to model all speech can also be explored.) Second, the basic GMM model is extended to directly include discrimination. For this, Support Vector machines (SVMs) are used (although any binary classifiers could be used for the discrimination subsystem).
Consider a single feature vector x. The desired posterior probability is expanded as:
Although the expansion is formally similar to that of equation (2) (both are just expressions of the formula for total probability, and require only that the events Cj be mutually exclusive and complete), the meaning is rather different. In equation (2), the Cj are simply a partition of the density for a given speaker, and are only required to account for data generated by that speaker; the underlying assumption is that a particular observed vector x is generated by one and only one of the Cj. In equation (8), the underlying assumption is that a given x is still generated by one and only one of the Cj (mutual exclusivity) and the probabilities P(Cj) still sum to one (completeness), but the Cj are no longer associated with only a single speaker. However, one may still think of them as clusters, in the sense that a given vector x can be attached to a single cluster by maximum likelihood (barring the unlikely event that x has the same maximum likelihood in more that one cluster).
At this point, the two terms appearing in the sum in equation (8) are examined. The discrimination will be built into the first term, which is modeled as:
where yij(x) is the output of an SVM, given x as an input, and where the SVM has been trained to distinguish speaker i from all other speakers, so that a more positive yij indicates more evidence in favor of speaker i. Equation (9) is an approximation: given some feature vector x associated with cluster Cj, the probability that it was generated by speaker i is modeled using a binary classifier, trained to distinguish feature vectors from speaker i (in cluster j) from feature vectors from all other speakers in cluster j. The assumption is that the information about the identity of speaker i that is encapsulated in the feature vector x can be extracted and represented by a real-valued function y(x). However this is exactly what a real-valued binary classifier is supposed to do. Clearly in the case where the data x are separable, in such a way that the class labels are in 1-1 correspondence with the speakers, the approximation becomes exact (the above probabilities become 0 or 1). At the opposite extreme, when a given vector x is equally likely to have been generated by any one of the target speakers, the yij are also not likely to be significantly more positive for any particular speaker i than for any other speaker, leading to equal P(Si|yij(x), Cj). In the intermediate case, where the densities overlap, but where discrimination is still possible, equation (9) essentially uses the classifier alone as a method for function approximation, where the function being approximated is the posterior probability.
Again, using Bayes' rule, the following can be written:
Since the y are real-valued outputs of a binary classifier, the denominator can be written as the sum of two densities, one corresponding to outputs from the target speaker Si, and one corresponding to outputs from all non-target speakers, denoted {overscore (S)}i:
Thus, the desired posterior probability becomes (the role of γ is discussed further below):
Note that p(yij(x)|Si,Cj) and p(yij(x)|{overscore (S)}i,Cj) are easily modeled using the outputs of the classifier, since they are one dimensional densities which are straightforward to estimate using a validation set, in contrast to the modeling of multidimensional densities. The P(Si|Cj) and P(Cj) can be estimated by counting cluster memberships. Note that if the training set is dominated by a small subset of speakers, P(Si|Cj) and P(Si) will vary significantly, and in some applications this may be useful prior knowledge. However, if it is known that the application is one in which P(Si) will vary significantly over time scales longer than that over which the training data was collected, in the absence of a model for such variation, and assuming that the system is not constantly retrained, it is safest to adopt the "uninformative priors" position and assume that all P(Si) are equal.
As noted above, the inventive concept comprises the notion of using a single GMM, and performing discrimination within that GMM. Two examples are described below. In the first, the "target-speech" model, only speech from target speakers is used to create the single GMM. In the second, the "all-speech" model, a single GMM is trained using a large pool of speakers (possibly including the target speakers, possibly not), with the intent that the resulting GMM is a representative model for all speech from which targets are likely to be drawn. (Here, the idea is to train a GMM once using a very large amount of data (which is referred to herein as pool data, below), and then to use this as a fixed GMM for the speaker ID task). Note that in both methods, during the classifier training feature vectors from non-target speakers can be used to supplement the set of counterexamples. A key property shared by both approaches is that the method "factors out" much of the variation in the signal before it is presented to the classifiers: thus to the extent that the single GMM models phonemes, a given classifier, such as an SVM, is trained to distinguish a particular phoneme for one speaker from that of other speakers.
Usually, when using SVMs for multiclass problems, the decision is made based on heuristic measures, such as the distance from the separating hyperplane (e.g., see C. Cortes and V. Vapnik, "Support vector networks," Machine Learning, 20:273-297, 1995; and C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, 2(2):121-167, 1998). The methods described here have the advantage that the SVM outputs are combined in a principled way with the Gaussian density models. For the case when the classifier outputs are very noisy, as they are here, this is especially advantageous over the approach of directly using SVM classifiers on the preprocessed data.
The target-speech model combines advantages of both classifier and GNM approaches. In clusters where speaker discrimination is hard, the two distributions for the yij (for positive and negative examples) in equation (12) will overlap significantly, and the terms will be dominated by the priors P(Si|Cj), P(Cj) and the likelihoods p(x|Cj), reducing to a GMM-like solution; in clusters where speaker discrimination is easier, the term A(x, Si, Cj) in equation (13) will vary significantly, taking very small values for negative examples, and becoming O(1) for positive examples, and the method approaches that of classical pattern recognition. However, it is worth considering what the expansion represented by equation (8) means in this case. As in the standard technique, the clusters are found using only target speech, and the hope is that non-target speakers will get low likelihoods. In fact much of the work around GMMs centers on how to add discrimination on top of the basic GMM approach in order to compensate for the GMM's tendency to give high likelihoods to non-target speakers (e.g., see the above-cited article by Gish et al.). In the DGMM case, the basic model has two weapons for rejecting non-target speakers: low cluster likelihood, P(Cj|x), and low classifier outputs, P(Si|yij(x),Cj). Since the DGMM here models only target speech, the role of the term γ in equation (14) is to allow non-target speech to result in low likelihoods. The term γ can be viewed as a constant approximation to a sum over a set of other clusters, corresponding to data generated by non-target speakers.
The all-speech model method may be viewed as relying on the binary classifiers for discrimination, and using the Gaussian mixtures as an additional preprocessing step. The model has the advantage that it need not be recomputed given a new set of target speakers. (It should be noted that one might consider incremental training schemes, in which the GMM is further trained with the target speakers added to the pool.) Furthermore, a set of highest-likelihood counterexamples for classifier training can be computed once, and added to those computed from any speakers whose data became available after the GMM was trained. As before, the p(x|Cj) are modeled as Gaussian densities, and P(Cj) are estimated using cluster membership counts. In computing the posterior probability P(Si|x) in equation (12), P(Cj) in equation (14) can computed from all the data, and P(Si|Cj) in equation (13) from cluster membership counts using only the target data. Since the DGMM here models all speech from which target speech will be drawn, the role of the term γ in equation (14) is to give low likelihood to a non-speech signal, for example one that is dominated by channel noise. The term γ can thus be viewed as a constant approximation to a sum over a set of other clusters, corresponding to non-speech data.
The description above was for the case of a single feature vector x. However, the inventive concept can be extended to handle multiple feature vectors (from the same speaker). Again, it is desired to find that speaker Si for which P(Si|{tilde over (x)}) is maximized. Again assuming that the xi are independent:
If all priors P(Si) are equal then maximizing P(Si|{tilde over (x)}) over all Si is equivalent to maximizing Σk=1 m log P(Si|xk) Otherwise, one must find the Si that maximizes:
Training a DGMM thus proceeds in two steps: training the GMM, as described above (e.g., see the equations (4), (5) and (6) and respective text); and then using the resulting GMM to cluster the data to generate SVM training data. For a given cluster, and a given SVM (there are NS SVMs per cluster), the positive examples are generated in order of likelihood. Suppose speaker S's vectors are ordered by likelihood for a given cluster C. Furthermore, suppose nSC is the number of speaker S's vectors that fall into cluster C when that speaker's vectors are assigned to all NC clusters via maximum likelihood. The number of positive examples for the corresponding SVM is then taken to be max(nSC, nmin), where nmin is a user-chosen minimum number of positive examples required to train an SVM (e.g., assume that nmin=250). If it is assumed that the number of negative examples was fixed at 2000, the examples are again found by ordering the vectors for all speakers other than speaker S by likelihood for cluster C. Finally, after a given set of vectors have been set aside to train a given SVM, the next 100 negative and 100 positive examples (again ordered by likelihood) are used as a validation set to estimate the density in equation (13).
Note that this method is very easily extended to handle the equivalent of cohort speakers. One can simply add the (non-target) data to that on which the GMM was trained, before computing the SVM training data. Any data thus added that gets high enough likelihood scores will appear as negative examples for the SVMs. Finally, training speed and generalization performance can be improved by Mahalonobis renormalizing the data, according to:
prior to SVM training and testing. Here L is the lower triangular matrix used in the Cholseki decomposition of Σ=LLT. Note that this renormalization is not unique, in that there are other transformations that also result in x′T�x′=(x−μ)TΣ1(x−μ), but in our case this does not matter, since only dot products of the data appear in both SVM training and testing for the kernels used. Loosely speaking, this renormalization is removing variation from the data which is already accounted for by the GMM, thus easing the learning task faced by the SVM; note that the renormalized training data has zero mean and unit covariance matrix.
With respect to the test phase of a DGMM, clearly if all terms in the sum in equation (8) are allowed to appear, the test phase will be slow (e.g., for 20 speakers and 50 clusters, 1000 SVMs would have to be evaluated), and worse, SVMs will be applied to data for which they were not trained, in the sense that SVMs for all clusters will be applied to data whose likelihood exceeds a given threshold in only a few clusters. Recalling that a given SVM is associated with a particular cluster and a particular speaker (in the sense that its training data is generated via likelihood computations for that cluster, and the positive/negative labels for the data are determined by that speaker's identity), there are two possible remedies: (1) cluster the data via maximum likelihood, and then use the corresponding SVMs for each cluster, or (2) only use an SVM on a given vector if its likelihood (P(C|x) in equation (8) exceeds some user-chosen threshold. The latter method has been experimentally found to give better results, which are described below. Note that both methods require a means of handling vectors which fall below threshold. If one simply does not include them in the sum, then one is discarding evidence that this speaker is not a target speaker. On the other hand, if they are included by attaching a fixed penalty score for P(Si|xCj) in equation (8), then the resulting contribution to the sum can swamp the contribution from the above-threshold vectors. One simple heuristic solution to this is to penalize the mean log likelihood resulting from just the above-threshold vectors by an amount which depends on the fraction of vectors which fell below threshold. However, as described herein, only the simplest approach, that of discarding the below-threshold vectors is considered.
The above approach requires that one estimate the posterior probabilities P(Si|yij(x),Cj). As explained above, one method to do this is to express this quantity as a ratio of densities as in equation (13). This has the advantage of giving a principled way to incorporate the priors P(Si|Cj), but it requires that one estimate the one-dimensional densities of the support vector outputs. The solution to this is described below.
Denote the density of the input data by p(x). A support vector machine may be viewed as a real valued function y(x)=Σi αi zi K(x, xi)+b, where the α are positive Lagrange multipliers found by the training procedure, xi are the training points and ziε{�1} their polarities, and b is a threshold, also determined by training (for more details, see for example the above-mentioned articles by Cortes et al.; and Burges; and also V. Vapnik, "The Nature of Statistical Learning Theory," Springer-Verlag, New York, 1995). The sum over the kernel functions is in fact an inner product in some Hilbert space H between the mapped point φ(x)εH and some vector w εH (the mapping φ, and the space H, are both determined by the choice of kernel K). (In fact, K only determines the value of the inner product of two mapped vectors, according to K(x1,x2)=φ(x1)�φ(x2). For a given choice of K, in general neither the space H nor the mapping φ are uniquely determined.) The density of the random variable y in terms of p(x) and K may therefore be written as:
In general, exact computation of this density is not feasible, since p(x) is unknown. Even if p(x) were known, an exact expression may be elusive: one can view the kernel mapping as a mapping of the data to an n-surface in H (recall that x εRn), and this surface will in general have intrinsic curvature (e.g., see C. J. C. Burges, "Geometry and invariance in kernel based methods" in B. Scholkopf, C. J. C. Burges, and A. J. Smola, editors, "Advances in Kernel Methods: Support Vector Learning," MIT Press, 1998), so when one projects onto a given direction, p(y) may have contributions from quite different parts of the mapped surface, making evaluation of equation (18) difficult. In the special case when the mapped data is normally distributed in H, it is straightforward to show that y must then itself be a one dimensional Gaussian. Therefore, for the purposes of this paper it is assumed thaty has Gaussian distribution.
At this point, attention is directed to the figures, which illustrate some applications of the inventive concept. Other than the inventive concept, the elements shown in the figures are well-known and will not be described in detail. An illustrative flow chart embodying the principles of the invention is shown in FIG. 1 for use in a voice mail system. In step 50, a DGMM is trained on a set of target speakers (e.g., see the equations (4), (5) and (6) and respective text). As noted earlier, training involves the labeling and collection of sound clips of various people. For example, as a user retrieves voice mail messages, the user labels each message as being associated with a particular person. In step 60, testing is performed on newly received voice mail. In particular, as each newly received voice mail is stored, the corresponding sound clips are tested against the training data using the DGMM. Test results include an identification of a particular person (whose data was used in the above-mentioned training phase) and a confidence level as to the accuracy of the decision. (Although not described, the labeling process involves adding a "label" field to the storage parameters for a voice mail message. This label field is associated, e.g., via a separately stored data base (not shown) with particular individuals. Further examples of labeling and testing are described below in the context of some illustrative systems embodying the principles of the invention.)
An illustrative DGMM system 100 in accordance with the principles of the invention is shown in FIG. 2. DGMM 100 comprises personal computer (PC) 105, local area network (LAN) 101, private branch exchange (PBX) 115, which includes voice mail server (VMS) 110. It is assumed that PC 105 represents one of a number of client-type machines coupled to LAN 101. VMS 110, of PBX 115, is also coupled to LAN 101. PBX 115 receives communications via facility 116, which represents any one of a number of voice-switched lines, e.g., analog, T1, etc. Illustratively, PBX 115 receives telephone calls via facility 116 and, when necessary, provides voice mail functionality for the creation and storage of voice mail messages. For the purposes of this description, it is assumed that VMS 110 performs the above-described steps shown in FIG. 1.
As is known in the art, a user associated with PC 105 receives notification of newly received voice mail via LAN 101 by a respective notification message from VMS 110. For example, the notification message can be a "pop-up" icon, email message, etc. In accordance with the principles of the invention, each notification message comprises at least the following information: an identification field and a confidence level. In particular, VMS 110 tests each newly received voice mail message using a DGMM. If VMS 110 identifies a potential sender of the message, the identification field identifies the person and the confidence level provides an estimate as to the accuracy of the decision, or selection, of that person. If VMS 110 does not identify a potential sender, the identification field provides a suitable message, e.g., "not a target speaker."
In accordance with the inventive concept, the user associated with PC 105 can gradually build-up a collection of sound clips for use in identifying callers. For example, initially, each notification message sent by VMS 110 to the user will indicate the message "not a target speaker." However, the user then "labels" the associated voice mail by replying, i.e., sending a responsive message, back to VMS 110. The responsive message indicates the associated voice mail message and includes a name of a person. Upon receipt of the responsive message, VMS 110 creates a label for this newly identified person. Once sufficient sound clips have been collected for the newly identified person, VMS 110 begins training. (It should be noted that other techniques can be used for identifying and labeling sound clips for training. For example, an application program, which, upon execution in a client, recovers current stored voice mail message headers (e.g., a speech to text transcription of calling party and/or calling number, etc.) from VMS 110 for display in one column of a two column list form on a screen (not shown) of PC 105. The other column of the list allows entry by the user of a name of an associated person, i.e., a label for the voice mail. A "submit" button causes the list information to be sent back to VMS 110, which then performs training on the identified sound clips. Other than the idea of using such an application program in conjunction with the inventive concept, such application programs use standard programming techniques and, as such, will not be described herein. NetScape� is illustrative of an application suite that will serve as such a platform.) Subsequently received voice mail messages are then tested against the trained data.
Other illustrative systems are shown in FIGS. 3 and 4. These systems operate in a similar fashion to that shown in FIG. 2 and, as such, will not be described herein in the interests of brevity except as noted below. For example, it should be noted that in DGMM 200, of FIG. 3, VMS 210 is separate from, and separately coupled to, PBX 215. (It should also be noted that PBX 215 could communicate to VMS 210 via a LAN connection (not shown) to LAN 201.) With respect to FIG. 4, it is assumed that a user associated with telephone 305, which is coupled to PBX 315, performs the above-described retrieving and labeling of voice mail messages via the touch-tone keypad of telephone 305. In this context, identification and confidence levels for each newly received voice mail are generated via audio signals from VMS 310. For example, in addition to announcing the time and date of a newly received voice mail, VMS 310 announces the identification (if possible) and the associated confidence level.
FIG. 5 shows an illustrative voice mail server (VMS) 400 embodying the principles of the invention. VMS 400 comprises interface unit 405, which couples VMS 400 to, e.g., LAN 101. Processor 410 is a stored-program control processor and executes a DGMM program stored in memory 420. Storage element 415 provides storage for sound clips, label data, etc. Line 401 represents a suitable interface element for coupling to a PBX (not shown). This coupling can either be internal if VMS 400 is a part of the PBX (as shown in FIG. 2) or external, if VMS 400 uses a separate connection to the PBX (as shown in FIG. 3). (If the PBX communicates with VMS 400 via a LAN, then, obviously, line 401 is not necessary.)
It should be noted that although the inventive concept is illustratively shown in the context of a majority of the processing taking place in a voice mail server, the functional partitioning between a client and a server can be in any proportion. For example, training could be performed on a server, while the client performs testing (either using the data stored on the server, or periodically updating (from the server) a respective data base in the client), or training could also be performed on the client.
Other variations are possible. For example, thresholds used during the testing process can be varied as a function of detected system differences. One such example is varying the thresholds used to test the sound clip as a function of the telephone used to convey the voice mail from the calling party. In this example, the thresholds used if the telephone is coupled to the PBX can be lower than if a call from an outside line is detected. Phone identification can be performed using caller identification, e.g., by incorporating such information in priors.
The foregoing merely illustrates the principles of the invention and it will thus be appreciated that those skilled in the art will be able to devise numerous alternative arrangements which, although not explicitly described herein, embody the principles of the invention and are within its spirit and scope.
|Cited Patent
|Filing date
|Publication date
|Apr 7, 1993
|Dec 14, 1993
|Itt Corporation
|Automated sorting of voice messages through speaker spotting
|Dec 30, 1994
|Jun 10, 1997
|Purespeech, Inc.
|Automatic speech recognition
|Jun 14, 1996
|Sep 8, 1998
|Lucent Technologies Inc.
|Compilation of weighted finite-state transducers from decision trees
|Jun 7, 1995
|Nov 17, 1998
|Rutgers, The State University Of New Jersey
|Speaker verification system using decision fusion logic
|Apr 1, 1997
|Jan 19, 1999
|T-Netix, Inc.
|Blind clustering of data with application to speech processing systems
|May 27, 1997
|Sep 28, 1999
|At&T Corp
|System and method of recognizing an acoustic environment to adapt a set of based recognition models to the current acoustic environment for subsequent speech recognition
|Mar 31, 1998
|Feb 22, 2000
|Dragon Systems, Inc.
|Sequential, nonparametric speech recognition and speaker identification
|Mar 31, 1998
|Jan 9, 2001
|Interval Research Corporation
|System and method for automatic classification of speech based upon affective content
|Del Alamo et al., ("Discriminative training of GMM for speaker identification", 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 89-92).
|Slomka et al., ("A comparison of Gaussian Mixture and Multiple Binary Classifier Models for Speaker Verification", Australian, New Zealand Conference on Intelligent Information Systems, 1996, Nov. 18-20, 1996, pp. 316-319).*
|Citing Patent
|Filing date
|Publication date
|Jul 30, 1999
|Sep 16, 2003
|Microsoft Corporation
|Methods for routing items for communications based on a measure of criticality
|May 2, 2001
|May 17, 2005
|Verizon Corporate Services Group Inc.
|System and method for maximum benefit routing
|Feb 28, 2002
|Aug 23, 2005
|Hewlett-Packard Development Company, L.P.
|Handset identifier using support vector machines
|Jan 4, 2002
|May 2, 2006
|International Business Machines Corporation
|Speaker identification employing a confidence measure that uses statistical properties of N-best lists
|Sep 26, 2000
|May 8, 2007
|International Business Machines Corporation
|Lattice-based unsupervised maximum likelihood linear regression for speaker adaptation
|Mar 8, 2004
|Jun 19, 2007
|Microsoft Corporation
|Methods for routing items for communications based on a measure of criticality
|Sep 5, 2000
|Nov 13, 2007
|Verizon Corporate Services Group Inc.
|Systems and methods for using one-dimensional gaussian distributions to model speech
|Jul 15, 2003
|Feb 26, 2008
|Microsoft Corporation
|Methods for routing items for communications based on a measure of criticality
|May 19, 2002
|Sep 9, 2008
|International Business Machines Corporation
|Optimization of detection systems using a detection error tradeoff analysis criterion
|Aug 16, 2001
|Oct 14, 2008
|International Business Machines Corporation
|Methods and apparatus for the systematic adaptation of classification systems from sparse adaptation data
|Mar 8, 2004
|Oct 28, 2008
|Microsoft Corporation
|Integration of a computer-based message priority system with mobile electronic devices
|Nov 22, 2004
|Nov 4, 2008
|International Business Machines Corporation
|Method and apparatus for training a text independent speaker recognition system using speech data with text labels
|May 13, 2004
|Nov 18, 2008
|The United States Of America As Represented By The Director, National Security Agency, The
|Method of modeling single data class from multi-class data
|Jul 18, 2005
|Dec 9, 2008
|Microsoft Corporation
|Methods for routing items for communications based on a measure of criticality
|Jul 1, 2003
|May 26, 2009
|France Telecom
|Method and system for analysis of vocal signals for a compressed representation of speakers using a probability density representing resemblances between a vocal representation of the speaker in a predetermined model and a predetermined set of vocal representations reference speakers
|Jul 14, 2005
|Jul 14, 2009
|Aurilab, Llc
|Distributed pattern recognition training method and system
|Apr 4, 2002
|Sep 22, 2009
|West Services, Inc.
|System, method, and software for identifying historically related legal opinions
|May 30, 2006
|Nov 17, 2009
|West Services, Inc.
|System, method, and software for identifying historically related legal opinions
|Aug 13, 2004
|Apr 13, 2010
|Microsoft Corporation
|Method and system for summarizing a document
|May 13, 2006
|Aug 3, 2010
|International Business Machines Corporation
|Quantizing feature vectors in decision-making applications
|Jun 4, 2008
|Oct 12, 2010
|Nuance Communications, Inc.
|Method and apparatus for training a text independent speaker recognition system using speech data with text labels
|Nov 16, 2009
|Jul 19, 2011
|West Services, Inc.
|System, method, and software for identifying historically related legal cases
|May 21, 2003
|Apr 24, 2012
|Microsoft Corporation
|Method for automatically assigning priorities to documents and messages
|Nov 18, 2005
|Aug 21, 2012
|Microsoft Corporation
|Word clustering for input data
|Apr 3, 2010
|Sep 18, 2012
|International Business Machines Corporation
|Quantizing feature vectors in decision-making applications
|Apr 8, 2010
|Apr 30, 2013
|International Business Machines Corporation
|Compensation of intra-speaker variability in speaker diarization
|Dec 9, 2010
|Jan 21, 2014
|International Business Machines Corporation
|Model restructuring for client and server based automatic speech recognition
|Oct 27, 2008
|Nov 18, 2014
|Microsoft Corporation
|Integration of a computer-based message priority system with mobile electronic devices
|Mar 23, 2012
|Jun 2, 2015
|Speech Technology Center Limited
|System and method for identification of a speaker by phonograms of spontaneous oral speech and by using formant equalization using one vowel phoneme type
|Mar 21, 2003
|Aug 11, 2015
|Hewlett-Packard Development Company, L.P.
|Traversing firewalls
|Nov 29, 2012
|Dec 22, 2015
|Educational Testing Service
|Computer-implemented systems and methods for content scoring of spoken responses
|Nov 27, 2009
|Apr 12, 2016
|Nottingham Trent University
|Biometric identity verification for access control using a trained statistical classifier
|May 31, 2013
|Jun 14, 2016
|Nuance Communications, Inc.
|Method and apparatus for automatic speaker-based speech clustering
|Dec 7, 2010
|Jan 31, 2017
|Knowles Electronics, Llc
|Noise suppression assisted automatic speech recognition
|Dec 8, 2010
|Mar 7, 2017
|Microsoft Technology Licensing, Llc
|Using e-mail message characteristics for prioritization
|Dec 16, 2015
|Apr 18, 2017
|Dolby Laboratories Licensing Corporation
|Speaker identification using spatial information
|Sep 20, 2013
|Apr 18, 2017
|Cirrus Logic International Semiconductor Ltd.
|Speaker recognition
|Oct 4, 2013
|May 2, 2017
|Knowles Electronics, Llc
|Noise suppression for speech processing based on machine-learning mask estimation
|Aug 27, 2015
|Oct 24, 2017
|Knowles Electronics, Llc
|Multi-sourced noise suppression
|Aug 16, 2001
|Feb 20, 2003
|Ibm Corporation
|Methods and apparatus for the systematic adaptation of classification systems from sparse adaptation data
|Apr 4, 2002
|Mar 6, 2003
|Peter Jackson
|System, method, and software for identifying historically related legal opinions
|Jan 4, 2002
|Jul 10, 2003
|Ibm Corporation
|Speaker identification employing a confidence measure that uses statistical properties of N-best lists
|May 19, 2002
|Nov 20, 2003
|Ibm Corporation
|Optimization of detection systems using a detection error tradeoff analysis criterion
|Jul 15, 2003
|Jan 22, 2004
|Eric Horvitz
|Methods for routing items for communications based on a measure of criticality
|Mar 8, 2004
|Sep 2, 2004
|Eric Horvitz
|Integration of a computer-based message priority system with mobile electronic devices
|Mar 8, 2004
|Sep 2, 2004
|Eric Horvitz
|Methods for routing items for communications based on a measure of criticality
|Mar 21, 2003
|Sep 23, 2004
|Perkins Gregory Eugene
|Traversing firewalls
|Jan 13, 2005
|Aug 4, 2005
|Konica Minolta Medical & Graphic, Inc.
|Medical image processing apparatus
|Jul 18, 2005
|Nov 10, 2005
|Microsoft Corporation
|Methods for routing items for communications based on a measure of criticality
|Jul 14, 2005
|Jan 19, 2006
|Aurilab, Llc
|Distributed pattern recognition training method and system
|Aug 13, 2004
|Feb 16, 2006
|Microsoft Corporation
|Method and system for summarizing a document
|Oct 27, 2005
|Feb 23, 2006
|Microsoft Corporation
|Methods for routing items for communications based on a measure of criticality
|Nov 22, 2004
|May 25, 2006
|Jiri Navratil
|Method and apparatus for training a text independent speaker recognition system using speech data with text labels
|May 30, 2006
|Sep 14, 2006
|Peter Jackson
|System, method, and software for identifying historically related legal opinions
|Jul 1, 2003
|Nov 9, 2006
|France Telecom
|Method and system for analysis of vocal signals for a compressed representation of speakers
|Nov 18, 2005
|May 24, 2007
|Microsoft Corporation
|Word clustering for input data
|May 13, 2006
|Nov 15, 2007
|Chaudhari Upendra V
|Quantizing feature vectors in decision-making applications
|May 21, 2003
|Nov 22, 2007
|Eric Horvitz
|Method for automatically assigning priorities to documents and messages
|Jun 4, 2008
|Sep 25, 2008
|Jiri Navratil
|Method and apparatus for training a text independent speaker recognition system using speech data with text labels
|Oct 27, 2008
|May 7, 2009
|Microsoft Corporation
|Integration of a computer-based message priority system with mobile electronic devices
|Mar 27, 2009
|Oct 8, 2009
|Vishwa Nath Gupta
|Media detection using acoustic recognition
|Nov 16, 2009
|May 20, 2010
|Peter Jackson
|System, method, and software for identifying historically related legal cases
|Apr 3, 2010
|Jul 29, 2010
|International Business Machines Corporation
|Quantizing feature vectors in decision-making applications
|Dec 9, 2010
|Jun 14, 2012
|International Business Machines Corporation
|Model restructuring for client and server based automatic speech recognition
|Nov 29, 2012
|Jun 20, 2013
|Educational Testing Service
|Computer-Implemented Systems and Methods for Content Scoring of Spoken Responses
|Sep 28, 2012
|Apr 3, 2014
|At&T Intellectual Property I, L.P.
|System and method for channel equalization using characteristics of an unknown signal
|Dec 13, 2013
|Jun 19, 2014
|Robert Bosch Gmbh
|System And Method For Event Summarization Using Observer Social Media Messages
|May 31, 2013
|Dec 4, 2014
|Nuance Communications, Inc.
|Method and Apparatus for Automatic Speaker-Based Speech Clustering
|Jan 28, 2015
|Aug 13, 2015
|Electronics And Telecommunications Research Institute
|System for speaker diarization based multilateral automatic speech translation system and its operating method, and apparatus supporting the same
|Sep 20, 2013
|Aug 4, 2016
|Agnito, S.L.
|Speaker Recognition
|Jul 1, 2003
|Feb 17, 2005
|France Telecom
|Method and system for analysis of vocal signals for a compressed representation of speakers
|Nov 3, 2010
|Apr 21, 2011
|Obschestvo S Ogranichennoi Otvetstvennost'yu �Centr Rechevyh Tehnologij�
|Method for identifying a speaker based on random speech phonograms using formant equalization
|May 23, 2016
|Mar 23, 2017
|Audio data detection method and system and storage medium
|U.S. Classification
|International Classification
|Cooperative Classification
|European Classification
|Feb 12, 1999
Owner name: LUCENT TECHNOLOGIES INC., NEW JERSEY
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:BURGES, CHRISTOPHER JOHN;REEL/FRAME:009769/0528
Effective date: 19980212
|Dec 2, 2005
|Fee payment
Year of fee payment: 4
|Feb 1, 2010
|Maintenance fee reminder mailed
|Jun 25, 2010
|Lapse for failure to pay maintenance fees
|Aug 17, 2010
|Expired due to failure to pay maintenance fee
Effective date: 20100625


text:
Ortiz helps Red Sox fan propose
PHOENIX – David Ortiz said he ran into a Red Sox fan last night in a parking lot. He said the fan, wearing an Ortiz jersey, came up to him and asked him if he could help propose to his girlfriend.
"He said 'I want to do something special for my girl. Can you help me?' I said sure. He said if he showed up early at the ballpark near the dugout that he would give me something to give to her."
When Ortiz noticed the man wearing a No. 34 Sox jersey, the man slipped Ortiz a box with the engagement ring, then brought his girlfriend over to meet Ortiz.
"I said to the woman, 'What do they call me?' She said, 'Big Papi!' What else? 'Mr. Clutch.'"
"I said, 'Well I have to come through right now once again.'"
That's when Papi presented her the ring on behalf of her boyfriend.
The woman accepted.
We were not able to get the couple's names because they were in the stands and we were on the field.

subset: commoncrawl


text:
Voice of Russia
March 19, 2014
Tokyo Electric Power Co said Wednesday it had suspended a radioactive water treatment system at the Fukushima Daiichi nuclear plant due to deteriorating performance. The operator found Tuesday that one of the three lines in the cleanup system did not eliminate radioactive material sufficiently.
Tokyo Electric halted all three lines called the Advanced Liquid Processing System (ALPS), but it did not detect any defects in the other two, said Akitsuka Kobayashi, a company spokesman.
The operator has been investigating the problem since Tuesday, Kobayashi said.


text:
Here we provide Ganesh Stotra V 2.0.1 for Android 4.0+ || GANESH STOTRA ||
Ganesh Stotra or Sankata Nashanam Ganapati Stotram is a prayer to Lord Ganesha which would destroy all sorrows. Ganesh Stotra is taken from the Narada Purana.
GANESH STOTRA is a very powerful shlokam. This shloka is chanted daily for three times (at least once) to get rid of all the SANKATAs (problems). All the problems get destroyed by chanting this stotra of Lord Ganesha. Number is 3 very dear to Lord Ganesha. Hence, it is desirable to chant this thrice if possible. Praying this Stotram will help to get knowledge, wealth, get salvation and all wishes fulfilled.
New Ganesh Stotra App has following features:
-Swipe Left-Right to navigate between each paragraph
-Use Next-Back buttons to navigate between each paragraph
2.Audio Voice-Over
-Listen to the sweet audio in background by singer Amruta Soman
-According to voice page will navigate automatically
-Use Music ON/OFF buttons to toggle between read mode and audio mode
3.Language Selection
-Sanskrit and English
if there is any problem please let us know. Write your problem in comment box below.
Editors Notes:
Very interesting game, I can spend a whole day to play, it is worth downloading, it is strongly recommended.


text:
The Lodge at Riverside
955 South East 7th Street, Grants Pass, OR 97526, United States of America –
Excellent location – rated 9.5/10! (score from 151 reviews)
Rated by guests after their stay at The Lodge at Riverside.
|Room Type
King Room
Max adults: 2
Max children: 2 (up to 12 years of age)
Queen Room with Two Queen Beds
King Room with River View
Max adults: 2
Max children: 2 (up to 12 years of age)
Queen Room with Two Queen Beds with River View
Area Info – Guests loved walking around the neighborhood!
Restaurants & Markets
- 7-11 Market 0.1 miles
- Safeway Supermarket 0.2 miles
- Wonder Bur Cafe/Bar 0.2 miles
- Roux 26 Restaurant 0.5 miles
- River\'s Edge Restaurant 3.5 miles
- Taprock NW Grill Restaurant 0 miles
Natural Beauty
- Rogue River River 0 miles
Closest Airports
- Rogue Valley Airport 23.7 miles
What would you like to know?No thanks
Thank you for your time
Your feedback will help us improve this feature for all of our customersClose
We're sorry, but there was an error submitting your comment. Please try again.Retry
4 Reasons to Choose The Lodge at Riverside
Why book with us?
Prices you can't beat!
Manage your bookings online
Booking is safe
Facilities of The Lodge at Riverside Great facilities! Review score, 9.1
Most Popular Facilities
- Picnic Area
- Outdoor Furniture
Pool and Spa
- Swimming Pool
- Pool Towels
- Pool With View
- Outdoor Pool (seasonal)
- Massage (additional charge)
- Wheelchair Accessible
Pets are not allowed.
Food & Drink
- Chocolate/Cookies
- Wine/Champagne
- Bar
- Restaurant
Free! WiFi is available in all areas and is free of charge.
Free! Free public parking is available on site (reservation is not needed).
- Accessible Parking
Front Desk Services
- Concierge Service
- Baggage Storage
- Express Check-in/Check-out
Cleaning Services
- Daily Housekeeping
- Dry Cleaning (additional charge)
Business Facilities
- Fax/Photocopying (additional charge)
- Business Center
- Meeting/Banquet Facilities (additional charge)
- Vending Machine (drinks)
- Designated Smoking Area
- Air Conditioning
- Heating
- Souvenir/Gift Shop
- Honeymoon Suite
- Family Rooms
- Facilities for Disabled Guests
- Non-smoking Rooms
- Newspapers
What topic(s) do you want to know more about?
Thanks for your help!
Your opinion helps us figure out what kinds of info we should ask properties for.Back to property
Whoops! It looks like something went wrong submitting this. Try again?Retry
Until 11:00 AM
Cancellation and prepayment policies vary according to room type. Please enter the dates of your stay and check what conditions apply to your preferred room.
Children and Extra Beds
All children are welcome.
Up to two children under 12 years stay free of charge when using existing beds.
Any additional older children or adults are charged USD 17.50 per night for extra beds.
The maximum number of extra beds in a room is 1.
Any type of extra bed is upon request and needs to be confirmed by management.
Additional fees are not calculated automatically in the total cost and will have to be paid for separately during your stay.
Pets are not allowed.
The Lodge at Riverside accepts these cards and reserves the right to temporarily hold an amount prior to arrival.
Guests are required to show a photo ID and credit card upon check-in. Please note that all Special Requests are subject to availability and additional charges may apply.
Please inform The Lodge at Riverside of your expected arrival time in advance. You can use the Special Requests box when booking, or contact the property directly using the contact details provided in your confirmation.


text:
You can also download a pdf version of the form here, which we would be grateful if you could complete and return to the Development Office. Thank you for your support.
In addition to the amounts and ways of giving illustrated on the online form, there are also opportunities to name a room.
Naming a room is a wonderful way of recognising generosity. It will give our new Arts Centre character and will remind future generations of the importance of philanthropy.
Rooms still available include:


text:
Smoky Mountain Memories – Part 2
On to Day 2 of our little escape to the mountains…
After taking a little stroll down the mountain that we were staying on to the playground, finding and capturing a lizard (fondly known as Lizzie), and huffing our way back up, we worked up an appetite.
If you've been to the Gatlinburg/Pigeon Forge area, you've probably heard of the Dixie Stampede. It came highly recommended, and we were not disappointed. The boys loved the horses and all of the action. The best part was kids 3 and under were FREE! (Which is a rare find; it's usually 2.)
The boys meeting their first Southern Belle (hopefully not the last). =)
Checking out the horses and riders after the show…
After sitting for a while, it was time to do some exploring. The older the boys get, the better the adventures get! It's so enjoyable to experience things through their eyes and take it in with them for the first time.
The Pigeon Forge River could have entertained them for weeks! (And ME for that matter!)
Isn't it stunning?
Brad entertained a dare from his sister to cross the river by hopping the rocks (it's just a few inches deep) without falling in. Some things between siblings never change!
High and Dry on the other side (and a tad proud of himself too)!
Hello Down There!
Hello Up There!
One of the most precious moments of the trip was after we returned to the cabin that evening and Henry sat on Grampa's lap on the porch for 20 minutes chatting non-stop about life and the great outdoors.
Do you have any little or big getaways planned for this summer? Where are you headed?


text:
On Mom's back
Griffon vulture
Happy face
Grey old man
Little one
A friendly face
Mike is the beautiful gray old boss at Amersfoort Zoo. Mike is 51 which is very old for a chimp. For years he was the leader of the group and he is still respected.
On Mom's back
On Mom's back Baby gorilla in Burgers zoo. Camera: Canon EOS 650D/Rebel T4i Tamron 70-300mm F/4-5.6 Di Canon Post processing: LightroomPhotoshop
Griffon vulture
Griffon Vulture in the zoo in Amersfoort
Akili, silver back gorilla. Photo taken in Artis, the zoo in Amsterdam.
Happy face
Chimp in Dierenpark Amersfoort.
A twin is always very special to me. Especially since I have twins myself, two sons. When I saw this trio this afternoon in Burgers ZooI I melted completely. Such a beautiful sight! Mother N'Gayla with her two kids N'Kato and N'Hasa. The twins were born June 13, 2013.
Grey old man
A monochrome version of a portrait of Mike the gray old beautiful chimpanzee in Dierenpark Amersfoort.
Lynx sitting on a rock
This is Bokito the famous silverback gorilla in Blijdorp, the zoo in Rotterdam.
Little one
Baby gorilla on mom's back in Artis, the zoo in Amsterdam. This is such a familiar pose. My son Simeon sucked his fingers like this. Gorilla's really are our closest relatives if you ask me.
A friendly face
The friendly silver back gorilla in Burgers Zoo, the zoo in Arnhem.
Portrait of Jambo, the leader of the gorilla group in the Apenheul in Apeldoorn.
Back to gallery


text:
About Us
With our firm commitment to provide top quality products and services to our clients at a competitive price, we Aqua Ion Exchange Systems are a pioneer manufacturer, exporter, supplier and service provider of wide assortment of Water Treatment Plants. Our product array comprises Mineral Water Plant,Reverse Osmosis Water Plant, Water Softener Plant, Demineralisation Plant, Sewage Treatment Plant, Effluent and Sewage Treatment Plant, Sand Activated Carbon and Iron Remover Filter, Domestic Water Purifiers, Water Treatment Plant Spares, Ion Exchange Resin and the likes. Commenced our business activity in the year 2004, we offer all our water treatment plants are developed with the help of ultramodern technology and it has a low treatment cost and customized to processes like ultraviolet treatment, bromination, iodination, and radial flow iron reduction for intensive biological treatments.
Our range of products are manufactured keeping in mind all the set guidelines of State Pollution Control Boards and other institutions. Our products are widely used in restaurants, malls, offices and homes. In addition to this we also offer Annual Maintenance Contract (AMC), Mineral Water Plant ISI Consultancy and Laboratory Setup Services. We are led by our owner Mr. Senthil Kumar, whose sharp business acumen and rich industry experience has helped us to maintain an fruitful relationship with our clients.
- Demineralisation Plant
- Effluent Treatment Plant
- Ion Exchange Resin
- Mineral Water Plant
- Modification & Services
- Reverse Osmosis Water Plant
- Sand Activated Carbon and Iron Remover Filter
- Sewage Treatment Plant
- Water Softener Plant
- Water Treatment Plant Spares
- Packaged Drinking Water Plant
- Pet Bottle Filling Machine
- Consultancy Services
- Domestic Water Purifiers
" We love to get enquiries from COIMBATORE ,ERODE & SALEM"


text:
Traveling Luck for Saint Francis Mission, Kentucky, United States
Where is Saint Francis Mission?What's around Saint Francis Mission?
Wikipedia near Saint Francis Mission
Where to stay near Saint Francis Mission
The timezone in Saint Francis Mission is America/Iqaluit
Sunrise at 08:26 and Sunset at 18:22. It's Dark
Latitude. 38.2078°, Longitude. -84.6967°
Weather near Saint Francis Mission; Report from Frankfort, Capital City Airport, KY 22.9km away
Weather :
Temperature: 5°C / 41°F
Wind: 10.4km/h South/Southwest
Cloud: Sky Clear
Weather :
Temperature: 5°C / 41°F
Wind: 10.4km/h South/Southwest
Cloud: Sky Clear
Satellite map around Saint Francis Mission
Loading map of Saint Francis Mission and it's surroudings
Geographic features & Photographs around Saint Francis Mission, in Kentucky, United States
- populated place;
- a city, town, village, or other agglomeration of buildings where people live and work.
- White Sulphur (0.7km)
- Sand Lick (5.3km)
- Watkinsville (5.8km)
- Woodlake (6.1km)
- Midway (7.9km)
- Duval (7.9km)
- Lancelot Estates (8.6km)
- Stamping Ground (8.8km)
- Great Crossing (9.9km)
- Davistown (10.3km)
- Peak (10.3km)
- Elsinore (10.8km)
- Forks of Elkhorn (10.9km)
- Duckers (11.1km)
- Slickway (11.4km)
- Nugent Crossroads (11.5km)
- Shiff (11.7km)
- Herdon (11.8km)
- Wallace (11.8km)
- Paynes Depot (11.9km)
- Jett (13.3km)
- Zion Hill (13.5km)
- Indian Hills (13.8km)
- Bon Air Hills (14km)
- stream;
- a body of running water moving to a lower level in a channel on land.
- McConnell Run (4.4km)
- Lee Branch (5.1km)
- Beals Run (5.8km)
- Blue Spring Branch (7km)
- Buck Run (7.6km)
- Lecomptes Run (7.9km)
- Jones Lane Branch (8.6km)
- East Fork McConnell Run (8.7km)
- Sebree Branch (8.7km)
- Cane Run (9km)
- Locust Fork (9km)
- Hickman Branch (10km)
- South Elkhorn Creek (11km)
- North Elkhorn Creek (11km)
- Slickway Branch (11.6km)
- McCracken Creek (12.4km)
- Dry Run (14.3km)
- cemetery;
- a burial place or ground.
- Masonic Cemetery (7.6km)
- Lindsay Cemetery (10.9km)
- Bond Cemetery (12.6km)
- Sunset Memorial Gardens (13km)
- Cedar Grove Cemetery (13.6km)
- Riddle Cemetery (13.6km)
- Zion Hill Cemetery (13.7km)
- Local Feature;
- A Nearby feature worthy of being marked on a map..
- Saint Francis Mission (0km)
- Spring Station (8.9km)
- Frankfort Country Club (14.1km)
- church;
- a building for public Christian worship.
- Zebulah Church (1.6km)
- Forks of Elkhorn Church (10.4km)
Accommodation around Saint Francis Mission
- school;
- building(s) where instruction in one or more branches of knowledge takes place.
- Orphans School (8.8km)
- Cardome Academy (14.1km)
- bridge;
- a structure erected across an obstacle such as a stream, road, etc., in order to carry roads, railroads, and pedestrians across.
- South Trimble Memorial Bridge (10.9km)
- ridge(s);
- a long narrow elevation with steep sides, and a more or less continuous crest.
- Pea Ridge (4.9km)
- airport;
- a place where aircraft regularly land and take off, with runways, navigational aids, and major facilities for the commercial handling of passengers and cargo.
- Marshall Field (14.2km)
- lake;
- a large inland body of standing water.
- Silver Lake (13.6km)
- tower;
- a high conspicuous structure, typically much higher than its diameter.
- WAXU-AM (Georgetown) (12.3km)
- mountain;
- an elevation standing high above the surrounding area with small summit area, steep slopes and local relief of 300m or more.
- Hickman Hill (9.5km)
Wikipedia entries close to
Saint Francis Mission
- Midway, Kentucky city city o
- Stamping Ground, Kentucky city city o
- Georgetown, Kentucky city city o
- Millville, Kentucky city city o
- Woodford Reserve landmark archaeologic
- Frankfort (Kentucky) city city h
- Kentucky State Capitol landmark government
- Versailles, Kentucky city city o
- Capital City Airport (Kentucky) airport airport
- Blue Grass Airport airport airport
- Sadieville, Kentucky city city o
- Georgetown-Scott County Airport airport airport
- Lawrenceburg, Kentucky city city o
- Commonwealth Stadium (Kentucky) landmark sport
- Lexington (Kentucky) city city d
- Monterey, Kentucky city city o
Airports close to Saint Francis Mission
Bowman fld(LOU), Louisville, Usa (104km)
Cincinnati northern kentucky international(CVG), Cincinnati, Usa (113.9km)
Cincinnati muni lunken fld(LUK), Cincinnati, Usa (125.1km)
Godman aaf(FTK), Fort knox, Usa (143.6km)
Indianapolis international(IND), Indianapolis, Usa (265.2km)


text:
|File Search
|Content Search
|Package version:
{\rtf1\ansi\ansicpg10000\deff0{\fonttbl{\f0\fswiss\fcharset77 Helvetica;}{\f1\fswiss\fcharset77 Helvetica-Bold;}{\f2\fswiss\fcharset0 Helvetica;}} \viewkind4\uc1\pard\tx1440\tx2880\tx4320\tx5760\tx7200\lang1031\f0\fs24\par \b\f1 kiki the nano bot\b0\f0\par \par Thanks a lot for downloading this program.\par \par \b\f1 package contents\b0\f0\par \par The package contains beside this Readme\ more»
kiki the nano bot ----------------- Thanks a lot for downloading this program. package contents ---------------- The package contains beside this Readme some documentation in HTML format and the binary for Windows, Mac OS X or the complete sources (depending on which package you downloaded). If you donwloaded the sources, please check the documents in the linux or macosx subdirecto more»
Building kodilib on Linux Prerequisites kodilib depends on some libraries and development tools which have to be installed on your system before you will be able to build it. I will just list these dependencies here, please check the net or your distribution's documentation how to obtain and install these tools and libraries. Development Tools You will need a vers more»
Building kiki on Linux Prerequisites kiki depends on some libraries and development tools more»
; Script generated by the Inno Setup Script Wizard. ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING more»
Big thanks for contributing to the creation of this game goes to the following people: Music more»
|kiki/ (12) 1928+ mirrors


text:
Pornhub is the TeenCurves - Brazilian Booty Jade Jantzen Takes A Huge Load 1,188,714 views. Skinny black teen sedcuced 788 days ago XXXDessert Skinny brunette teen girl 788 days ago XXXonXXX. Brazilian Archives - Miami Butt Sluts1. Sexy goth teen Jade Starr is an horror slut - fetish masturbation with a black dildo video.
My normal weight is 160 and i would like to get a brazilian butt lift with tummy Miami Plastic Surgeon. Sort movies by Most Relevant and catch the best full length Hot Brazilian Milf movies now! Casual encounters - south florida casual encounters - craigslist.
Try Cartoon Sex Porn Tube is a team of lustful people who treasure real emotions Furry Toons Fucking! Im thinking of choosing DR FISHER from Miami despite the bad reviews about Vanity Cosmetics customer service.
New Wave Latinas Hot Milf Monique Fuentes Sexy Blonde Milf Riding His Man For Cum. This is a clip from something I shot with this Brazilian MILF while back in Miami.


text:
Business Directory-ONLY for businesses in Castle Cary, or within a 5-10 mile radius, and at the discretion of the Webmaster/Promotion & Communication Committee. Please submit your listing with as much info as possible, especially email address & website/Facebook listing. Listings from outside the area will not be published.
Brook Cottage 3.5 miles
Twin bedroom with ensuite bathroom. No animals, no smokers. Contact Mrs Susan Fone.
Send Message to listing owner
Brook Cottage 3.5 miles


text:
This mod is going to have a campaign right?
This member has provided no bio about themself...
This mod is going to have a campaign right?
This keeps looking better and better guys really amazing stuff here, cant wait to play.
Forgive me if it's something obvious, i know very little about ships, but I'm just curious, what are those large machine like metal-devices? Also this looks great by the way.
Just when I thought it was starting to look amazing before! Now you go and do this!! :D
Your work is f*****ing amazing my friend. If you can be on the bridge of the ship during the entire landing sequence that would be so awesome and epic. Outside views would be cool as an option too, but the thought of actually being able to be on the bridge and experience the ship crashing through the atmosphere all the way to the surface of the planet almost puts a tear in my eye :,D even if there has to be a few loading screens during it :P (even with loading screens its probably not possible with the engine but I will keep dreaming anyway XD )
Wow man, I really love watching your work improve as this mod comes along, this chamber is looking VERY GOOD. It all has the right mood and feel to it. It needs some haze or mist and the ceiling looks like it could use some work but I am impressed.
Wow that looks great!
I like this picture a lot, I like the mood and the atmosphere you've created here. The stalagmites could use a little work but the rock wall textures are really improving.
Damn now that's cool! Great work man, its looking better all the time!
This is just absurd. That anyone thinks they have the right to stop someone from creating something that they truly love, **** WARNER BROS. I refuse to purchase any game or film that has any involvement with Warner Bros until this has been turned around and made right. Everything in this video is absolutely correct and I support you guys to the fullest extant.
:D sounds really great! Its probably impossible, but how awesome would it be to be on the ship while its landing on the planet, rocketing through the clouds, rain, and lightening.
That looks really awesome.. i just want to be able to walk around the whole inside of the Prometheus free roam style while its in space and while its on the ground preferably :D
This mod looks great BTW and so does the model and texture's of Helm's Deep. But, the interior layout of the Hornburg Courtyard isn't quite right here. Im quite well acquainted and familiar with the architecture of Helm's Deep and many other locations of Middle Earth as I've been obsessed with Tolkien for years and have built multiple scale models of more than a few locations which I researched extremely thourougly. Please contact me if you would like some blueprints or other architectural input or assistance.


text: New Zealand | 3 months ago Fletcher Building makes inroads Source Fletcher Building has confirmed it will buy construction firm Higgins in a $315 million deal, and announced a restructure of its overall business. While rumours of the impending sale have been doing the rounds for months, both parties have previously kept mum. The NZX-listed Fletcher posted a notice to the stock market on Tuesday morning, saying the deal was expected to be completed around June 30. It said it would take over Higgins Group Holdings and related assets, including a Fiji contracting business. READ MORE: Fletcher Building looking to expand through acquisition Higgins has more than 1200 staff, and is the third largest player in the road construction and maintenance sector. Its owners, the Manawatu-based Higgins family, consistently appear on the National Business Review's rich list, with wealth recently estimated at $150m. Fletcher Building chief executive officer Mark Adamson said Higgins was a logical choice, as the two companies had a history of working together for more than 25 years. "We have signalled for some time our desire to extend our activities into the road construction and maintenance sector, where we have identified significant opportunity." Higgins generated $391m of revenue and a $35m operating profit in the 2015 financial year, which is forecast to hit $40m in 2016. Fletcher said it expected the transaction, funded from existing cash and debt facilities, to increase earnings a share from its first year. The deal will see the company acquire asphalt and bitumen plants, 16 operational quarries, and other related businesses including traffic sign manufacturing. Higgins' ready-mix concrete and property businesses are not included. Adamson said the Fiji arm of the Higgins business would complement Fletcher's existing activities in the area. "We will be looking to extend the sphere of operations beyond Fiji into other South Pacific territories over the next few years." The agreement is conditional on a number of factors including Overseas Investment Office and Commerce Commission consent. Hamilton Hindin Greene adviser Grant Davies said the deal was not huge relative to Fletcher's scale, and would not stretch its balance sheet. "It's a $315m acquisition from a $5 billion company." Davies said the market was cautiously optimistic about the news, with Fletcher's share price up 1.4 per cent to $7.10. "Hopefully they've learned the lesson from a few of the acquisitions they've made recently, and are focusing on the right areas." Fletcher also announced changes to its divisional structure and leadership team, to be led by newly appointed chief transformation officer Lee Finney. The company will be organised in five divisions; building products, international, distribution, residential and land development, and construction. Adamson said the new structure reflected changes to the portfolio, with a number of businesses divested over the past two years. The company's focus was on investing where it saw the strongest opportunity to further grow profits, while gaining operating efficiencies from grouping similar activities together. - Stuff


text:
Management / Public Relations
NVP Started as a humble company specializing in the representation of emerging Visual Storyteller and Photojournalists. However, in 2007 we began a journey into a completely new realm of Event Management and Public Relations. we have since been involved in several major local and international events, such as:
The Eastern Breeze International Festival, (EBIFF) is an independent biannual film festival held in Toronto, Canada, which previews short and mid-length films of all genres, including documentary, animation, fiction and experimental, from around the world. The last two installments of the EBIFF were managed, and publicized by New Vision Productions Inc. Ashes to Ashes theater, Trilogy and Beyond Frames visual exhibitions and Six Weeks of Art Show Festival in Canada was also publicized by NVP. Over the last decade we have attracted over 20,000 spectators, and attendees; and over one million virtual visitors have visited our projects online. Moreover, we have proudly received over 400 articles, reports, and interviews from local and international media; in paper, television, and radio format.
Our understanding of the importance of public relations has been a crucial component to our success. Today we are active participants of the international artistic developments, and passionate ambassadors to the arts. Furthermore, we at NVP are always ready to receive new management tasks, and to offer our experience and expertise in the organization, and management of future exhibits.


text:
نصيحة تداول الخيارات الثنائية By Angelica Recierdo, The Annotated Museالمواقع
http://highstxray.com.au/?pyzo=%D9%83%D9%8A%D9%81%D9%8A%D8%A9-%D9%83%D8%B3%D8%A8-%D8%A7%D9%84%D9%85%D8%A7%D9%84-%D8%AD%D9%82%D8%A7-%D8%B9%D9%84%D9%89-%D8%A7%D9%84%D8%A7%D9%86%D8%AA%D8%B1%D9%86%D8%AA&408=9a Often, I prefer leisurely physical activities: A scenic bike ride, yoga or a light jog. Sometimes I feel the need for a more intense workout and will willingly undergo the short-term tyranny of a Zumba instructor. Upon learning we would be hiking during our service spring break trip to Colorado, I was genuinely excited for breathtaking views. "No, you don't need to buy hiking boots," the trip leaders said. As an East Coast, sea-level native, I was relieved, if only for a moment.نصائح الخيارات الثنائية
الخيارات الثنائية التداول بلوق We rented a car that had New Jersey license plates, already labeling us as tourists. Seven Northeastern students optimistically packed shorts and dresses, believing the weather in Colorado would be better than in Boston. We were wrong, and it couldn't be more glaringly obvious than when we found ourselves braking hesitantly before a sign saying "No Winter Maintenance" on the road leading up to the trail we wanted to hike.الخيارات الثنائية رديت
انتقل إلى هؤلاء الرجال I cursed my no-traction sneakers every trudge up and down that mountainous trail. It was a strange feeling of balancing awe at the nature around me and fear of slipping on the snowy tracks. Fellow hikers were dressed like they were embarking on Arctic expeditions. They had poles from REI, and I used makeshift ones from branches. Despite the struggles, it is an incomparable feeling to stand on top of the world and have all worries and concerns shrink beneath you. It was a welcome change of scenery from tall city buildings.جميع الاسهم السعودية تداول اليوم
المكافآت Our strenuous hikes prepared us for our service work with Project C.U.R.E., the largest donor of medical supplies and equipment to resource-poor countries. In a large, cold warehouse, America's medical excesses arrive to have a second life. In countries like Rwanda or Armenia, an infusion set will be used to give lifesaving hydration therapy or a baby will be supplied with many disposable diapers. We spent our week unloading trucks full of donations, sorting them and packaging them with care and medical expertise in tow.سعر الذهب اليوم في السعودية بالجرام
انقر فوق هذا الموقع We made Kits for Kids, knapsacks filled with basic health supplies for children like Neosporin and Band-Aids. We wrote uplifting messages on them like "you are beautiful and unique" لماذا لا تحاول هذه and وظائف خيار ثنائي or "keep learning and playing." I felt humbled to know every catheter and oxygen mask I touched was going to save a life. I felt myself subconsciously including my cumulative nursing knowledge into the packages. With every gauze pad, I thought of a nurse applying pressure to stop the bleeding in a clinic with a dirt floor. With every set of ECG stickers, I thought of a sick heart miles away from the nearest hospital or nations away from the nearest cardiac specialty hospital.الضرائب الخيارات الثنائية
تلميحات أكثر فائدة By the looks of it, Project C.U.R.E. will always be backed up on donations. IV poles and crutches lay cluttered in dusty aisles and gowns and gloves wait in unopened boxes to be donned by life-saving clinicians. We were told that we were their most efficient team. But the bins we worked so hard to empty will fill up again by the next day, as is the reality of medical need abroad.
الخيارات الثنائية نيوزيلندي التداول Americans travel to volunteer because we have the means to. My spring break was as "voluntourism" as it gets. Getting a new profile picture was as much a motivation as making a difference. And even though our hike was just for fun, I have to realize that may not be the case for the patients receiving our supplies in various rough terrain around the world. We insert ourselves into uncomfortable situations to become better people. Meaningful service and appreciation can manifest when self-awareness is intact at all times.


text:
Rebekka Rumpel
Rebekka Rumpel is a research assistant with the Africa Programme at Chatham House.
Her work focuses on the political economy of East Africa, with particular focus on Kenya and Tanzania.
Rebekka holds an MA in Global Governance and Diplomacy from the University of Oxford, and a BA in Politics and International Relations from the University of Cambridge.


text:
The mistakes of the past haunt Lucy Flaud, who years ago stopped attending the activities for courting-age young people in her hometown of Bird-in-Hand. Now 25 and solidly past the age of Amish courtship, Lucy has given up any hope of marriage, instead focusing her efforts on volunteering in both the Plain and fancy communities of Lancaster County. Yet no matter how hard Lucy strives, she feels uncertain that she'll ever find redemption.
Dale Wyeth has a deep mistrust of modern-day "advances" and the dependency they create. The young Englisher's interest in living off the grid is fueled further when he meets Christian Flaud, Lucy's father. Dale appreciates the self-sufficient ways of the Old Order Amish, and Christian invites him to learn more about them by staying at the family farm.
As Christian and Dale grow closer, developing a father-son rapport, Lucy begins to question what Dale's being there might mean for her. Could God be testing her? Or is it possible that even the most unworthy heart - and two people from very different walks of life - can somehow find a new beginning?
©2016 Beverly M. Lewis, Inc. (P)2016 Recorded Books
"Slow but Good"
Overall, it was good. The story telling was much slower but it worked due to the setting of the Amish culture. Seemed rather redundant in regards to repeated explanations for the reasoning behind the main character's behaviors. Ending was good though and wrapped up the story nicely.
"save your money"
the story was mundane and very slow, the narrator never changes voice inflection, just kind of read the story with animation, I gave up around chapter 6.
"Beautiful story"
This was such a beautiful story of Lucy finding her way back to God's living arms, even though she realized she had been in His arms the whole time. I also loved the little side stories that blended in so well helping her along the way.
"Love at last"
It is great to meet a young woman whose faith in God grows and changes her while she grieves her past misdemeanours . This story is one that warms the cockles of ones heart as we hear a number of interconnecting stories of days gone by and the strength of true love. I really enjoyed this story and felt regret at finishing it.
"Mixed bag..."
Yes, Christina Moore is the perfect narrator for all Beverly Lewis books, and I've listened to almost all of them that she has narrated. She has the Amish accent down pat!
Hearing the story behind the main character's pain...
It was enjoyable but I never felt much emotional investment.
Report Inappropriate Content
If you find this review inappropriate and think it should be removed from our site, let us know. This report will be reviewed by Audible and we will take appropriate action.


text: PRIVATE LENDING BRUCE E. DINGER Essentially, private money lending is a lender's opportunity to become the bank, reaping the profits just like a bank would. It's a great way to generate cash flow and produce a predictable income stream - while at the same time, provide excellent security and safety for your principle investment. You can do what the banks have been doing for years…make a profitable return on investments backed by real estate. There is no other investment vehicle like it. Through private money lending, lender's have the opportunity to become the bank HOW THE PROCESS WORKS The process is simple. The Residential Redeveloper (RR) finds an extremely undervalued property to purchase - and once lender gives the green light, the RR borrows the funds to purchase and renovate the property. At closing, the lender receives a mortgage on the home along with other important documents. Next stage is the property renovation. Once the renovations are complete (typically 3-6 months depending on the size of the project), the RR will list and sell the property. When it's time for closing, the lender receives his principle plus 10% interest payment. It's just that simple! The goal is to keep turning that money for the lender and keep them making substantial profits so they keep coming back – building a long term mutually beneficial relationship. OVERVIEW OF THE PRIVATE LENDING PROCESS


text:
Rgbtrade is a Web-based marketplace for the RGB Community Currency. It is based on ecological and social principles such as energy, recycling, fairness, transparency, and responsibility. It is a tool for sustainable development. First, you need a community and the will to use this RGB Community Currency. Technically, it requires a typical Web hosting environment with PHP and MySQL. Participants can place ads and make transfers using the RGB-currency unit. All information except email addresses is world-viewable.
A tool for securing DNS communications between a client and a DNS resolver.
A polyhedral extraction tool (PET).


text:
Ocean Basket (Melrose Arch), Melrose Arch
Limited Listing
This listing is a limited member on WiningAndDining. This means that unfortunately we can not display all the information that we would like to.
Full members get:
Full members get:
- A full description.
- A menu displayed.
- A map to the restaurant.
- A virtual tour.
- Filtered reviews of the restaurant.
- The ability to email and send an sms to make a reservation.
This restaurant is closed or has been removed
Restaurant description
Restaurant description
Ocean Basket is South Africa's favourite chain of seafood restaurants. There's one near you, where you will find friendly people, a home-from-home feeling, and great value for your hardearned bucks. Some Ocean Baskets have a fresh-fish deli too, and you can order to eat in or takeaway, whatever grabs you.
One thing's for sure. You will find only the...
Suggested restaurants nearby Ocean Basket (Melrose Arch), Melrose Arch
011 218 4000
Protea Hotel Fire & Ice! - Melrose Arch[African, Americas, Steak house]
Melrose Arch , Johannesburg
011 6841477
Moyo (Melrose Arch)[African, South African]
Melrose Arch , Johannesburg
011 684 1616
Melrose Arch , Johannesburg
011 452 9706
Fahrenheit Seafood and Grill (Edenvale)[Steak house, Seafood]
Edenvale , Johannesburg
011 788 8777
Parea Taverna[Greek, Vegetarian, Seafood]
Illovo , Johannesburg
011 245 4846
Parc Ferme[Seafood, Asian, Sushi]
Sandton , Johannesburg
087 940 3880
Signature Restaurant (Sandton)[South African, Seafood, International]
Sandton , Johannesburg
011 234 1085
Luca's Ristorante Italiano[Italian, Pizza, Seafood]
Sunninghill , Johannesburg


text:
"How to submit a manuscript to Avalonia for consideration"
Avalonia is currently considering manuscript for publication in the Autumn/Winter 2018/2019.
PLEASE READ the following information carefully if you wish to submit your book project to Avalonia for consideration. Avalonia DOES NOT accept unsolicited manuscripts.
Avalonia is a small independent esoteric press based in the UK. Avalonia is known for publishing specialised works within the field of occult and magical research, the history of goddess spirituality, Pagan traditions, folk magic, ceremonial magic, Eastern mysticism, grimoires, witchcraft and related practices. If you are not familiar with two or more of the Avalonia titles, please first explore our catalogue and consider reading some of the Avalonia titles before deciding if your project is suitable for publication by ourselves.
Avalonia only accepts a small number of manuscripts for publication each year, in all instances these works will be selected on individual merit with the intention that it should fit within the existing catalogue.
Avalonia is currently considering manuscripts for publication in the Autumn / Winter of 2018/2019.
Content is important for Avalonia. Our readers are interested in scholarly material covering specialist areas related to the modern and ancient worship of Gods and Goddesses, the Western Mystery Tradition, folklore, the grimoire tradition and alternative/green living. Additionally practical work from established practitioners and teachers with an established following within the aforementioned subject areas might also be considered, especially if their work has an experiential (modern) approach, whilst being rooted in history or tradition.
Avalonia DOES NOT publish modern "spell books" or "books of shadows", channelled or received texts, works which are attacks on other works, beliefs or traditions, poetry or art books, children books, personal journeys, fiction or books on topics which does not fit within that outlined above.
Avalonia prefers to work with authors who:
- have previously published work,
- have an established (3-5000 monthly views, or 3000+ followers/likes) social media presence,
- are familiar with two or more of our current catalogue titles,
- are aware that successful non-fiction author are both able and willing to promote their work to their readership, have a genuine passion and knowledge of their subject and want to convey this to their readership.
Still feel that your work will suit the Avalonia catalogue? Still interested in publishing with Avalonia?
Great, I look forward to hearing from you!
In the first instance, please email email@example.com with an outline (200-300 words) of the project you would like us to consider. Include with this an author biography, detailing your previous work and experience; as well as an overview of the kind of readers your work is aimed at, and details of any competing works.
How to prepare (format) your Manuscript: Avalonia prefers to receive manuscripts in MSWord format. Images should be supplied separately, preferably as JPEG at 300dpi, interior images will be converted to grayscale. Keep formatting simple: use only bold, underline and italics, and "normal". Please use the "footnote" function to insert footnotes. If your manuscript is accepted for publication it will be typeset into a style suitable for publication by Avalonia.
Also please note: Avalonia is a small independent publisher, and is unable to pay advances. Our authors are paid a generous royalty on sales, bi-annually, but there are no guarantees of earnings. Furthermore, Avalonia is driven by a desire for knowledge and expansion of the esoteric horizons, not by profit alone.
DO NOT send attachments – we will hit "delete" at once.
DO NOT send us your manuscripts in the post – it will go straight into the recycling if you do.


text:
1996 Chevrolet Corvette Coupe - Consumer Review
Build Quality
Reliability Value
Build Quality
Reliability Value
5 of 5 people found this review helpful
1996 Collector Corvette
By Joe St. Romain on
Vehicle1996 Chevrolet Corvette 2dr Coupe
ReviewI've bought this Vette new in 1996, and drive it about 6000 exciting miles per year. It is bullet proof sound. I never worry about it leaving me. Other than oil changes, the ONLY maint. on it was 1 set of brake pads, 2 sets of tires, 1 water pump, and 2 batteries. This is a VERY reliable car that can be driven hard if you want to.
Best FeaturesDelco Bose Gold sound system, automatic climate control, and fantastic seats highlight the interior. A beautiful look that still turns heads after 10 years with the 5 spoke wheels highlights the exterior. Superior performance and gas economy (yes! gas economy) highlight the drivetrain.
Worst FeaturesIt is perfect as is. Get one if you can, but plan on answering the question, "Why are you smiling so much?"
Recommend (5) (0)
Are you the author of this review?Update Your Review
Comments (0) Post a Comment
You must be signed in to post a comment.


text:
Armed police storm flats after reports of young gang with gun
ARMED police have stormed a block of flats after reports that a gang of youths were reportedly spotted with a gun.
Police were called to Etchingham Road, Eastbourne, just after 8pm on Tuesday.
Officers and a helicopter surrounded the of flats in Sussex following reports of a group of youths with baseball bats and a possible gun.
The youngsters were reported to have been seen running into a block of flats when police arrived.
Officers swiftly surrounded the building and warned warned nearby residents to remain in their homes until the incident was resolved.
Just before 10.10pm, armed police moved into the block and within minutes removed a man from one of the flats.
A police dog was sent in to search the remainder of the premises, but there was nobody else inside.
Busy late shift assisting @sussex_police at Beachy Head and Eastbourne. Thanks @NPAS_Redhill for the fuel pic.twitter.com/ss84uEgon6— NPAS Wattisham (@NPAS_Wattisham) May 3, 2016
A 34-year-old man was arrested on suspicion of possessing an imitation firearm with intent to cause fear of violence and affray.
He remained in custody on Tuesday night.
Detective Superintendent Nick May, from the East Sussex Police, said: "We will continue with our investigation locally to establish the circumstances – which includes discovering who else was involved.
"However, we do not believe that they are a threat to the public and people can now move freely in the area again.
"The scene has been secured and a marked vehicle will remain in place overnight before we resume our search of the premises in the morning."


text:
You will be full of energy over the next 12 months but think carefully about how you use it. Just because you can steamroller your way through opposition does not mean you have to. In the long-term co-operation always works best.
ARIES (March 21 - April 20):
For best results today you should be totally honest about your feelings, even in situations where a backlash is likely. You may lose out in some way in the short-term but in the long-term your reputation will soar.
TAURUS (April 21 - May 21):
A loved one can be hard to understand at times and today it may be impossible to work out what is going on in their head, never mind their heart! Don't worry, it will all come right in the end. It always does.
GEMINI (May 22 - June 21):
Make sure any changes you make over the next 24 hours are absolutely necessary. If you have any doubts at all you should keep things as they are, at least for the time being. Sometimes, doing nothing is best.
CANCER (June 22 - July 23):
Make a wish list and expect those wishes to start coming true over the next few days. Thoughts exist, they are real, and the more you think about what you desire the more likely it is you will get it.
LEO (July 24 - Aug. 23):
You may not be the sort who is easily fooled but you should still be on your guard today. Someone you thought you could trust is playing games at your expense. Don't let their mischief leave you out of pocket.
VIRGO (Aug. 24 - Sept. 23):
The planets warn you may be too lenient with someone and if you are it is likely they will take it as a sign of weakness – and take from you even more. Don't let your heart rule your head, at least not today.
LIBRA (Sept. 24 - Oct. 23):
Under no circumstances should you take risks with money. The sun's move into the financial area of your chart means there is a lot to gain but also a lot to lose, so make sure you know what you are doing.
SCORPIO (Oct. 24 - Nov. 22):
If there is one thing you hate it's not being sure what is true and what is false, but there is no way of knowing today. The more thought you give the situation the more of a dilemma it will be – so quit thinking!
SAGITTARIUS (Nov. 23 - Dec. 21):
Some people just cannot take criticism and one such person will give you a hard time today, just because you pointed out to them where they are going wrong. They should not abuse you – they should thank you.
CAPRICORN (Dec. 22 - Jan. 20):
You seem to be in a forgiving mood at the moment, which is not like you at all. Others may be suspicious but, yes, it's true – you really do want to be nice to people. They may even be nice back to you.
AQUARIUS (Jan. 21 - Feb. 19):
You have never been the sort to care too much what others think and if you find yourself swimming against the tide of opinion today you will be quite happy about it. You enjoy being the odd one out.
PISCES (Feb. 20 - Mar. 20):
If certain people seem somewhat evasive it's a sure sign that something underhand is going on – and you have a fair idea what it is. Don't let on about it yet though. Don't let them know their deceit has failed.
Visit Sally at www.sallybrompton.com
For your extended forecast, call 1-900-783-8333, or 1-877-976-1414 for credit-card access. Calls cost $1.49 per minute.


text:
Chatt Hills is located in Georgia.
Chatt Hills, Georgia has a population of2,366.
The median household income in Chatt Hills, Georgia is
The median household income for the surrounding county is $56,642
compared to the national median of $53,482.
The median age of people living in Chatt Hills is
32.5 years.
The average high temperature in July is 90.2
degrees, with an average low temperature in January of 30.2 degrees.
The average rainfall is approximately 51.5 inches
per year, with 1.6 inches of snow per year.
Real Living Georgia Life Realty
211 Main Street
Villa Rica, GA
(770) 456-2600
Real Estate Agents & Staff
Real Living Capital City
1799 Briarcliff Rd., Ste. Q2
Atlanta, GA
(404) 874-9094
Real Estate Agents & Staff


text:
DLT Update. Mair éad Martin June 28, 2007. DLT Infrastructure. Annual Lifecycle 170 DLT public workstations; 200 Library workstations, laptops, 17 printers Receipt of hardware Imaging development, testing and installation
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Mairéad Martin
June 28, 2007