text:
Raymond Marchakitus
unknown - January 15, 2012
Raymond Marchakitus, 71, passed away on Sunday, Jan. 15, 2012, of a heart attack following a brief battle with cancer. At the time of his death, Ray was surrounded by his family and his companion Molly Subers. Ray was born on July 4, 1940 and raised in Plymouth, PA. He graduated from Wilkes College in Wilkes-Barre, PA with a bachelor's degree. He worked in Washington, D.C., for the Federal Housing Administration and the Small Business Administration, retiring in 1997. Ray enjoyed golf, walks at Green Lakes and visits with his children and grandchildren.
A Memorial Service will be held on 11 a.m. Tuesday, Jan. 24, at Good Shepherd Lutheran Church, 7248 Highbridge Rd., Fayetteville.
In lieu of flowers contributions may be made to Good Shepherd Lutheran Church Memorial Fund, or the American Cancer Society.
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Mike Bell: College Football Notes From Cocktail Napkins…
College Football's silly season is in full swing as Florida's AD Jeremy Foley gets his man in Gainesville and The Huskers make an uninspiring hire. At first it looked Foley might have blown it on former Bama Offensive […]
Heisman Watch: Wisconsin's Melvin Gordon Ready For Heisman Showdown With Abdullah
This week, Melvin Gordon leads the Wisconsin running game against another Heisman candidate and his ground game, Nebraska and Ameer Abdullah. The running back showcase should double as this week's Heisman Trophy Spotlight Game.
Rick & Jamie All Star Committee Rankings
1) Mississippi State 2) Florida State 3) Oregon 4) Alabama 5) TCU 6) Arizona State 7) Ohio State 8) Baylor 9) Auburn 10) Ole Miss 11) Kansas State 12) Michigan State 13) Nebraska 14) Notre […]
American Photojournalist Who Contracted Ebola Working In Liberia Lands In Nebraska
A plane carrying an American photojournalist who contracted Ebola while working in Liberia landed Monday in Nebraska, where he will undergo treatment for the deadly disease.
Heisman Watch: Why Michigan State Should Fear Ameer Abdullah Of Nebraska
Nebraska has three Heisman Trophy winners in its storied football history. Running back Ameer Abdullah has a shot to become the school's fourth Heisman winner.
College Football Spotlight: College Football's Best Month Will Get Started With A Bang
October is the best month for college football, and it is getting started on the right foot this weekend. The first weekend in October will serve up six games between top 25 teams, which will be a nice breath of fresh air after the last few weeks.
Nebraska Beats No. 23 Georgia 24-19 In Gator Bowl
JACKSONVILLE, Fla. (AP) Tommy Armstrong Jr. connected with Quincy Enunwa for two touchdowns, including a 99-yarder in the third quarter, and Nebraska held on to beat No. 23 Georgia 24-19 in the rain-soaked Gator Bowl on Wednesday. Playing in […]
Gator Bowl 2014 Has A Familiar Feeling
Last year at this time Georgia was coming off a very disappointing loss in the SEC Championship to Alabama and soon getting ready to play Nebraska in the Capital One Bowl. This year there was […]
No. 23 Georgia, Nebraska Rematch In Gator Bowl
JACKSONVILLE, Fla. (AP) — Georgia and Nebraska will play in the postseason for the second straight year. The Bulldogs and Cornhuskers were paired in the Gator Bowl on Sunday, a rematch of last year's Capital […]
Pete Fiutak, College Football News, "Marcus Mariota Is The Best College Football Player In America"
Pete Fiutak drops by for his weekly visit to talk all things college football including the bad weekend in college football, Marcus Mariota, and even the Trent Richardson trade. . . .

subset: commoncrawl


text:
The New Performance Enhancer: A Gluten Free Diet?
EAST HARTFORD, Conn., July 16 /PRNewswire/ — Celiacs, you have company. Tour de France-racing company.
In 2008, the Garmin-Transitions US Cycling Team experimented with a gluten free diet as a way to improve performance by reducing inflammation, which is important for weeks-long races. The experiment was a success.
"My performance really improved a lot — there was definitely a correlation," rider Tom Danielson told Men's Journal. "I think that my digestion is better, and because of that my sleep is better and my recovery is better."
Bakery On Main's Gluten Free Granolas in Nutty Cranberry Maple, Extreme Fruit & Nut and Apple Raisin Walnut, were selected for inclusion in the Garmin-Transitions US Cycling Team's gluten free anti-inflammatory diet by the team chiropractor, Matthew Rabin.
"Absolutely love the products," said Rabin. "Exactly what would be beneficial for our riders and sits perfectly alongside our dietary ethos."
Individuals with Celiac disease must follow a lifelong gluten free diet, and many believe that a gluten free diet can be beneficial to children with Autism. Recently the gluten free diet has gained popularity as a healthier way of eating, but the Garmin-Transitions cycling team is the first to publicly implement it to improve athletic performance.
"I created our gluten free granola as a way to give Celiacs something that was good for them but tastes like it isn't," said Bakery On Main founder, Michael Smulders. "It's exciting to learn that there are other ways our gluten free granola can be good for you."
For more information, please contact email@example.com.
Bakery On Main was founded in 2003 with a simple philosophy: food that's good for you should taste good too. It all started in the small bakery of a natural foods market on Main Street in Glastonbury, Connecticut. Founder Michael Smulders listened to his Celiac customers complain about many of the gluten free options available to them, and made it his mission to create products that were good for them but taste like they aren't. Today, the company's products are manufactured in a state-of-the-art facility just a few miles from the original bakery and are widely available in the U.S., Canada and the UK. For more information, visit www.bakeryonmain.com.
SOURCE Bakery On Main


text:
Apple Help
It's free to register, to post a question or to start / join a discussion
Non-smartphone that can sync contacts with a Mac
Likes # 0
Posted September 23, 2012 at 10:29AM
Does anyone know if there's a simple non-smartphone on the market that can sync contacts with a Mac? Reason is that I want a simple phone for personal use without the distractions of email, Facebook etc, but don't want to have to manually keep the contacts up-to-date. Thanks!
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Posted September 23, 2012 at 5:09PM
Dont know the answer to your question but you are truly asking for problems using your email address as a username on an open forum.
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Posted September 23, 2012 at 7:02PM
Gah! Thanks for letting me know. I didn't realise that had happened but I've just fixed it.
Reply to this topic
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text:
The Grants for Arts Projects (GAP)
- Type: Foundations
- Source: National Endowment For The Arts
- Visit Website
- Award Min: $0.00
- Award Max: $0.00
- Quantity: Unknown
- Deadline: May 24, 2012
In most areas, funding is limited to organizations. (Direct awards to individuals are made only through Literature Fellowships, NEA Jazz Masters Fellowships, NEA Opera Honors, and NEA National Heritage Fellowships in the Folk & Traditional Arts.)
The FY 2013 Grants For Arts Projects Guidelines, Including The Art Works And Challenge America Fast-Track Categories Have Been Posted. Deadlines: Art Works - March 8 And August 9, 2012; Challenge America - May 24, 2012. Click On A Discipline/field Below For More Specific Information. The FY 2012 Our Town Guidelines Have Been Posted. Deadline: March 1, 2012. Federal Partnership Opportunities Such As The National Arts And Humanities Youth Program Awards Are Listed Within The Disciplines/fields. Artist Communities Arts Education Dance Design Folk & Traditional Arts Literature Local Arts Agencies Media Arts Museums Music Opera Presenting State And Regional Theater & Musical Theater Visual Arts
Additional Info:
Have questions? Should I contact Grants.gov or the NEA? You should contact Grants.gov for: •ALL questions related to registration (e.g., help in completing all of the steps in registration; checking on your registration status; verifying or updating your registration information). •Help in applying through Grants.gov, i.e., the mechanics of getting your application through the system. Grants.gov Contact Information The Grants.gov Contact Center is available 24 hours a day, 7 days a week Telephone:1-800-518-4726 E-mail: firstname.lastname@example.com Web site: www.grants.gov You should contact the Arts Endowment for: •Questions about your specific project or the content of your application (e.g., eligibility, the appropriate discipline/field for your project, project breadth, consortium projects). Please contact the Arts Endowment ONLY after you have reviewed the guidelines and the Frequently Asked Questions. You should contact the staff for the category and/or discipline that is most appropriate for your project.


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A speaker recognition apparatus employs a non-parametric baseline algorithm for speaker recognition which characterizes a given speaker's speech patterns by a set of speech feature vectors, and generates match scores which are sums of a ScoreA set equal to the average of the minimum Euclidean squared distance between the unknown speech frame and all reference frames of a given speaker over all frames of the unknown input, and ScoreB set equal to the average of the minimum Euclidean squared distance between each frame of the reference set to all frames of the unknown input. The performance on a queue of talkers is further improved by normalization of reference message match distances. The improved baseline algorithm addresses the co-channel problem of speaker spotting when plural speech signals are intermixed on the same channel by using a union of reference sets for pairs of speakers as the reference set for a co-channel signal, and/or by conversational state modelling.
1. In a method of automatically recognizing a speaker on a communication channel, including the steps of digitizing input speech signals into a series of frames of digital data representing the input speech, analyzing the speech frames by a speaker recognition module which compares the incoming speech to a reference set of speech features of a given group of different speakers obtained during prior training sessions and generates respective match score therefrom, and determining which speaker the input speech is identified with based upon the match scores with each speaker associated with at least one stored reference frame, in combination therewith, the improvement wherein:
said analysis of speech frames by said speaker recognition module is implemented through the use of a set of speech feature vectors to characterize a given speaker's speech patterns, said speech feature vectors being non-parametric in nature and
said comparison of incoming speech to reference speech features by said speaker recognition module includes generating a match score which is a sum of a ScoreA set equal to the average of the minimum Euclidean squared distance between the unknown speech frame and all reference frames of a given speaker over all frames of the unknown input, and ScoreB set equal to the average of the minimum Euclidean squared distance between each frame of the reference set to all frames of the unknown input, over all frames of the reference set of speech features,
wherein the "distance" from uj to the reference message R is: ##EQU15## and the "distance" from ri to the unknown message U is: ##EQU16## wherein uj is the j-th frame of unknown message U and ri be the i-th frame of reference message R, and ##EQU17## and wherein said comparison of incoming speech to reference speech features includes a step of normalizing said match score with respect to said stored reference frame for all speakers to provide a normalized score and comparing all normalized scores for all speakers to select the speaker having a highest acceptable normalized score.
2. A method of speaker recognition according to claim 1, further including the step of normalizing the match scores relative to the reference group of speakers.
3. A method of speaker recognition according to claim 1, further including the step of removing the effects of variations in reference message content by using the z score to remove the means and fix the variance of an unknown queue of match distances to each reference message.
4. A method of speaker recognition according to claim 1, further including the step of controlling the sensitivity of the speaker recognition model by selecting speech frames for inclusion or exclusion depending upon the expected speech information contained therein.
5. A method of speaker recognition according to claim 1, further including the step of normalizing the frames of input speech to remove the mean channel spectrum observed within the message, by the process known as blind deconvolution.
6. A method of speaker recognition according to claim 1, further including the step of determining a speaker speaking in a conversation of a number of speakers by using a union of reference sets for pairs of talkers as the reference set for a co-channel signal.
7. A method of message sorting according to claim 1, further including the step of using a non-parametric comparison of input speech messages to the reference set for the given group of speakers, wherein the reference set characterizes a given speaker's speech patterns by a non-parametric set of speech feature vectors.
8. In a method of automatically sorting voice messages transmitted on a communication channel, including the steps of placing input speech messages in a queue, digitizing the input speech messages into a series of frames of digital data representing the input speech messages, analyzing the speech frames by a speaker recognition module which compares each incoming speech message to a reference set of speech features of a given group of different speakers obtained during prior training sessions and generates respective match score therefrom with each speaker associated with at least one stored reference frame, and determining which speaker the input speech message is identified with based upon the match scores, in combination therewith, the improvement wherein:
said analysis of speech frames by said speaker recognition module is implemented through the use of a set of speech feature vectors to characterize a given speaker's speech patterns, said speech feature vectors being non-parametric in nature and
said comparison of incoming speech to reference speech features by said speaker recognition module includes generating a match score which is a sum of a ScoreA set equal to the average of the minimum Euclidean squared distance between the unknown speech frame and all reference frames of a given speaker over all frames of the unknown input, and ScoreB set equal to the average of the minimum Euclidean squared distance between each frame of the reference set to all frames of the unknown input, over all frames of the reference set of speech features,
wherein the "distance" from uj to the reference message R is: ##EQU18## and the "distance" from ri to the unknown message U is; ##EQU19## wherein uj is the j-th frame of unknown message U and ri be the i-th frame of reference message R, and
and wherein said comparison of incoming speech to reference speech features includes a step of normalizing said match score with respect to said stored reference frame for all speakers to provide a normalized score and comparing all normalized scores for all speakers to select the speaker having a highest acceptable normalized score.
This is a continuation of application Ser. No. 07/922,139, filed on Jul. 30, 1992, which is a continuation of application Ser. No. 07/699,217, filed on May 13, 1991, now abandoned.
The present invention relates to automated speaker spotting, and particularly, to a system for automated sorting of voice messages through speaker recognition using improved recognition processing techniques.
Automatic algorithms for speaker recognition have been developed as early as 1963. In one early study by S. Pruzansky, described in "Pattern-Matching Procedure For Automatic Talker Recognition", J. Acoust. Soc. Amer., vol. 35, pp. 354-358, 1963, the information contained in digital spectrograms of words excerpted from sentences was used as the feature set in comparing utterances of different speakers. In a further study on the same data, a methodology was proposed for selecting features formed by averaging the speech energy within rectangular areas of the spectrograms of selected words. The general conclusion was that the effectiveness of such features is reduced by averaging across frequency and increased by averaging across time. Despite the counterintuitive nature of these findings, the merit of long-term averaging was confirmed in a more recent and much larger study by S. Furui, "Comparison Of Speaker Recognition Methods Using Statistical Features And Dynamic Features", IEEE Trans. ASSP, vol. 29, pp. 342-350, June 1981.
In 1974, studies investigated the use of linear predictive parameters as features for speaker recognition. This work was motivated by the observation that the predictor coefficients, together with pitch and voicing, were sufficient to regenerate high-quality synthetic speech. A minimum distance rule was used for recognition and the inference vectors were long-term time averages of the various linear predictive parameters. Cepstral coefficients were found to give the best accuracy. On clean speech, speaker recognition methods based on linear prediction were shown in one study by R. E. Wohlford, E. H. Wrench, and B. P. Landell, "A Comparison Of Four Techniques For Automatic Speaker Recognition", Proc. IEEE ICASSP, no. 3, pp. 908-911, 1980, to outperform methods based on power spectral or cepstral analysis. This work was done on studio-quality speech, and more recent work indicates that this conclusion does not hold for telephone speech. A difficulty in applying linear predictive analysis to telephone speech is that such analysis is highly sensitive to narrow-bandwidth signals (tones), even when they are of very low amplitude.
In 1985, studies by P. Rajasekaran and G. Doddington, "Speech Recognition In The F-16 Cockpit Using Principal Spectral Components", Proc. ICASSP-85, vol. 2. p. 882, 1985, performed linear prediction analysis by the autocorrelation method. It then performs Fourier spectral analysis on the impulse response of the prediction filter, and computes a filterbank representation of the power spectral envelope. The principal spectral components method was later applied to speaker verification by J. Naik and G. Doddington, "Evaluation Of High Performance Speaker Verification System For Access Control", Proc. ICASSP, pp. 2392-2395, 1987. Improved accuracy was obtained by replacing the principal components transformation with a "clustering" transformation. Similar findings have been reported in other studies. The motivation of this approach is that it selects features based on their discrimination power rather than their variance.
A number of studies have been done to select a relatively small set of features that efficiently characterizes a speaker. In one study, it was found that the fundamental frequency parameters led all others in terms of discrimination ratio. This conclusion was shown to be invalid in a later study, which found practically no discrimination power in fundamental frequency. The later study showed the importance to speech data when appreciable time elapsed between the training and test samples. One study in the area of speech recognition by C. Olano, "An Investigation Of Spectral Match Statistics Using A Phonetically Marked Data Base", Proc. ICASSP-83, 1983, describes an evaluation procedure for developing a filterbank and match statistic to be used in a keyword recognition system.
In general, the research work in speaker recognition was focused at an early stage on developing "compact" and "efficient" measurements and models. A principal problem is how to handle extremely large numbers of statistics without exceeding the capabilities of speech processing machines, even despite the enormous increases in computer memory and processing capacity of machines today. In the long run, however, efforts to develop parsimonious representations may have only limited progress. Instead, a primary goal should be to improve performance, as opposed to reducing computation.
The early work in speaker recognition, such as that of Pruzansky discussed above, represented speakers as points in a multi-dimensional measurement space. New speech material to be recognized was processed to form another point in this same space. Recognition was performed by determining the distance to each speaker, and selecting the speaker with the minimum distance. A significant variation of the minimum-distance approach was reported by K. P. Li and E. H. Wrench, "An Approach To Text-Independent Speaker Recognition With Short Utterances", Proc. IEEE ICASSP, pp. 262-265, April 1983. Each speaker was modeled as a set of 40 points in the measurement space. These 40 points were frames from the speaker's training material that were chosen based on histograms of their distances to other training frames of the same speaker and different speakers. The system used a multi-modal, rather than a uni-modal, speaker model, minimized the effect of speech events not observed in the training data, and chose each speaker's model to discriminate that speaker from other speakers.
A similar method developed by F. Soong, A. Rosenberg, L. Rabiner, and B. Juang, "A Vector Quantization Approach To Speaker Recognition", Proc. ICASSP-85, Vol. 1, pp. 387-390; 1985, was based on vector quantization. A vector quantization codebook was created for each speaker from a clustering analysis of the training data. Test speech was then encoded using all codebooks, accumulating the coding distortion for each frame. The speaker whose codebook gave the lowest average distortion was recognized. A. L. Higgins and R. E. Wohlford, "A New Method Of Text Independent Speaker Recognition", Proc. ICASSP-86, vol. 2, no. 1, April 1986, developed a generalization of this method in which the individual frames in the codebook were replaced by multiple-frame segments. Recognition was performed by a template-matching algorithm, where the multiple-frame segments were used as templates. This method was compared with the vector quantization method and shown to give better text-independent speaker recognition accuracy.
The weaknesses of all these variants of minimum-distance multi-modal modeling are: (1) they require estimation of many free parameters, (2) the number of training examples associated with each mode is uncontrolled, and (3) the significance of deviations between the input speech and the modes is, unknown. The template-matching method is found to be quite sensitive to noise and other channel distortions.
The first use of statistical models in speaker recognition was reported in a study by A. B. Poritz, "Linear Predictive Hidden Markov Models And The Speech Signal", Proc. ICASSP-82, pp. 1291-1294, 1982. Speech production was modeled as an underlying, or hidden, Markov process in which outputs were generated with various probability density functions (PDFs) depending on the state of the model. Parameters of this hidden Markov model were assumed to be characteristic of the speaker.
In work by R. Schwartz, S. Roucos, and M. Berouti, statistical models were developed in which each speaker was characterized by a single multivariate PDF of individual frame feature vectors. They applied standard statistical pattern recognition procedures to estimation of PDFs and classification of speakers. In one method, each speaker's PDF was assumed to be multivariate Gaussian, and its parameters, the mean, and the covariance matrix, were estimated. The probability of each unknown frame was evaluated using this PDF, and the product of probabilities over all unknown frames was computed. The result was an estimate, assuming independent frames, of the likelihood of the observed unknown being produced by the model speaker.
Another method tested was a non-parametric PDF estimation technique. This technique computed the distance between each unknown frame and all the frames of training data for a given speaker. The likelihood of the frame was estimated using Parzen's method multiplied by a weighting factor. The Parzen estimate of the local density is inversely proportional to the volume of a hypersphere centered at the test point and just enclosing the kth-nearest training frame, where k is chosen depending on the total number of training frames. Again, the product of probabilities over all unknown frames was computed to produce an estimate, assuming independent frames, of the likelihood of the observed unknown being produced by the model speaker. The two probabilistic classifiers produced comparable recognition accuracy, which was clearly better than that of a minimum-distance method. However, it is found that the accuracy of Parzen estimates deteriorates rapidly as the dimensionality of the measurement space increases, so that the non-parametric is not found to be better than the parametric method.
Recently, R. Rose and D. Reynolds, "Text Independent Speaker Identification Using Automatic Acoustic Segmentation", Proc. ICASSP, pp. 293-296, 1990, reported development of a speaker identification algorithm based on parametric PDF estimation using a Gaussian mixture model. The density model is a weighted linear combination of Gaussian densities, each with a common diagonal covariance matrix. For a given number of component Gaussian densities, the algorithm estimates the weights, means, and variance terms using an unsupervised iterative maximum likelihood technique. The simulation results were excellent, outperforming the standard unimodal Gaussian model by a large margin.
Speaker verification was first tested using telephone channels in 1975 by A. B. Rosenberg, "Evaluation Of An Automatic Speaker-Verification System Over Telephone Lines", Bell System Tech. J., vol. 55, pp. 723-744, 1976. The system's initial rejection rates were about 20-25%, probably because enrollment was done in a single session. As the system was used, template adaptation reduced the error rate to a stable value of about 5%. A telephone line impairment that was observed to be definitely detrimental was pulse-like background noise. Such noises can be induced at the telephone exchange by switching currents in neighboring equipment. An excellent report on the signal degradations resulting from telephone transmission and their effects on speech recognition was written by L. S. Moye, in "Study Of The Effects On Speech Analysis Of The Types Of Degradation Occurring In Telephony", STL Monograph, pp. 1-238, Standard Telecommunication Laboratories, Harlow, England, July 1979.
H. Gish, et al., in "Methods And Experiments Of Text-Independent Speaker Recognition Over Telephone Channels", Proc. ICASSP-86, April 1986, reported on text-independent speaker recognition using the Gaussian modeling technique discussed above. When speaker testing data was taken from the same telephone call as their training data, accuracy in one test was 90%. When the same test was repeated using training and test data from different calls, accuracy decreased to 61%. About one-third of this loss was recovered through a combination of channel modeling, in which a correction factor was applied to the model covariance used in the mean term of the likelihood equation, and experimental adjustment of the weights assigned to the mean and covariance terms. Recently,, Gish developed a speaker identification method based on a metric for comparison of covariance matrices, described in "Robust Discrimination In Automatic Speaker Identification", Proc. ICASSP, pp. 289-292, 1990. Computationally, the metric involves a summation over a number of terms. Gish reports on the performance of a family of such metrics, which differ in the terms included in this summation. Excellent closed-set identification performance is shown for the best metric. There is, however, no way to determine a-priori which specific metric is best to use in a particular case. A further difficulty is that the method is a minimum-distance method, providing no information about the likelihood of the observations for a given speaker.
A principal feature of the present invention is the use of a baseline algorithm for speaker recognition which is non-parametric in nature and makes no assumption about the statistical distributions of speech features. The reference data used to characterize a given talker's speech patterns are a large set of speech feature vectors, not a set of estimated distribution parameters. By abandoning the use of assumed statistical models, i.e. the parametric approach, an order of magnitude improvement in speaker spotting performance can be obtained. Previous parametric models do not adequately represent the complexities of the true distribution of speech features. The further development of parametric methods, in the direction of more complicated distributions which might approximate true speech more accurately, have the difficulty of estimating an increased number of statistical parameters which such models entail. Although statistical models of speech may be developed in the future which are more accurate by virtue of a more suitable functional form, rather than by having more parametric degrees of freedom, it was appropriate to seek improvement in current speaker recognition systems by the non-parametric baseline algorithm of the present invention.
The non-parametric baseline algorithm is used in a speaker spotting system for the sorting of voice messages in telephone transmissions. The new algorithm achieves particularly promising results in speaker spotting in the co-channel mode of telephone transmission. The co-channel problem of speaker spotting, when two or more speech signals are intermixed on the same audio channel, is solved by using a simple union of reference data sets for pairs of talkers as the reference for a co-channel signal, and extending the score normalization procedures to sets of such models. This simple solution to the co-channel problem is made feasible by the power of the basic non-parametric algorithm in spotting speakers, and can be readily extended to telephone quality speech.
In accordance with the invention, non-parametric algorithms are developed for spotting the occurrences of known, "target" talkers within conversational telephone messages. The messages are sampled from a potentially open, population of talkers, and are made up of unrestricted extemporaneous speech. The algorithm will process messages in a message queue, producing a score for each message. The score is used to reorder the messages in the queue so that the messages containing "key" events are more likely to appear at the front of the queue than at the rear. The sorting algorithm is embedded in an integrated software system capable of accepting digitized waveform files as input. Rather than sorting voice models given an input message, the sorting algorithm attempts to sort input messages in search of a given voice. In both sorting tasks, pairings of voice models and input messages are to be ranked in order of the probability that the voice model matches the actual talker, given the acoustic observations. These probabilities cannot be directly estimated by comparing the input speech with the voice models. Rather, such a comparison provides a score related to the likelihood of the observations being produced by the model speaker. These two probabilities are related by Bayes rule. ##EQU1##
The denominator, p (X), is the probability of the input acoustic observation. In the case where voice models are being sorted given an input message, the denominator is constant, and the two probabilities are proportional. Thus, the likelihood of the observations with respect to the model is a sufficient statistic for the identification task. Where messages are being sorted, on the other hand, p(X) varies from one message to another, and must be estimated. This is a very important point that has not been addressed in previous work.
The algorithm of the present invention approximates p(X) as a sum of conditional probabilities of the message being produced by each individual talker in a set of reference talkers. ##EQU2## In this approximation, the finite set of reference talkers Si, 1≦i≦N, is used to represent the actual talker population, which is unlimited. Explicitly modeling the open speaker population in this way greatly improves performance of speaker verification.
The above objects and further features and advantages of the invention are described in detail below in conjunction with the drawings, of which:
FIG. 1 is a block diagram of the basic steps of the non-parametric speaker sorting baseline algorithm in accordance with the invention.
FIG. 2 is a block diagram of the message preprocessing portion of the baseline algorithm.
FIG. 3 is a schematic diagram illustrating the message matching and scoring process.
FIG. 4 is a schematic diagram illustrating the score normalization process.
FIG. 5 is a diagram illustrating the baseline algorithm performance for representative telephone data sets.
FIG. 6 is a diagram illustrating the algorithm performance for simulated co-channel telephone data.
The various features, advantages, and improvements of the invention are used in speaker recognition systems. Such systems are well known in the industry as generally having a front-end speech digitizer for digitizing audio speech input into a series of frames of digital data at a given sampling rate representing the input speech. The speech frames are analyzed by a speaker recognition module which compares the incoming speech input to reference set of speech features of different speakers obtained during prior training sessions. The speaker recognition module scores the closeness of matches between the input speech and the corresponding reference sets of different speakers the system has been trained to recognize. A decision is then made as to which speaker provides the closest match, and the input speech is then identified as belong to that speaker. In message sorting systems, input speech messages are placed in a queue and the messages are sorted using the speaker recognition module for speaker identification. In speaker spotting systems, input speech conversations of two or more speakers on a channel, such as a telephone line, are placed in a queue and the speech segments of the different speakers are identified using the speaker recognition module.
In the present invention, improvements to the baseline algorithm used in a conventional speaker recognition module are provided to enhance speaker recognition performance. A nonparametric approach to speaker recognition is used in the present invention, and the specific embodiments described below relate particularly to enhancement of the baseline algorithm in a message sorting system for speech transmitted on a telephone channel.
FIG. 1 is a block diagram of the baseline algorithm showing the processing steps of message preprocessing, intermessage matching, score normalization, and queue processing. The excellent performance of the baseline algorithm is attributable to several key properties. The speech features used have been found to be robust in many applications. Frames corrupted by noise and clipping are largely eliminated. Blind deconvolution is used to remove the effects of linear channel variability. The computed match distances are found to be highly correlated with log likelihood ratios. Further, the score normalization removes the residual biases and unknown scale factors of the match distances and unconditional message probabilities, as necessary for accurate sorting. The manner in which these and other properties of the baseline algorithm are obtained is discussed in the following description.
FIG. 2 is a block diagram of the message preprocessing portion of the baseline algorithm. The main functions are feature extraction, where frame amplitude and filterbank parameters are derived from the incoming speech signal, frame selection, and blind deconvolution. Each of the main functions is described below.
In feature extraction, the speech signal is sampled M times per second and converted to a 16-bit linear digital representation. This sampled data is processed by performing a 256-point DFT every twenty milliseconds (96 point overlap). A Hamming window is used, without pre-emphasis. Fourteen spectral channels are extracted from this analysis by forming weighted sums of individual DFT spectral power values. The fourteen channels span approximately 300 to 3500 Hertz and are constant bandwidth up to approximately 1000 Hertz, and constant Q thereafter. Each channel has very sharp cutoff (sharper at the upper boundary than at the lower), and crossover is at about the two dB point. Channel weighting is such that white noise input causes equal output in each channel. The frame power is computed, by summing the power in all DFT channels and converting the result to dB.
The spectral channel representation is normalized by first taking the fourth root of each channel power, and 12 -normalizing the fourteen component vector of spectral values. The fourth root used in these parameters was suggested by studies of the perception of loudness, and is very similer to a logarithmic transformation over the dynamic range of interest. The channel bandwidths and placements roughly correspond to equal intervals on the the mel scale of frequencies. These parameters are used in many applications of speech and speaker recognition, and are generally found to be robust with respect to noise degradation. They are not considered optimal for speaker spotting, and it is expected that the algorithm can be improved considerably by using better parameters.
In frame selection, three criteria are imposed to limit the set of frames on which the algorithm operates. First, frames of very low power are eliminated, in order to consider only that portion of the data sum that may contain speech. A histogram of power is then formed using all remaining frames in the message. A nominal interval of acceptable energies is established as the range from the tenth percentile value plus six dB to the 97th percentile minus three dB. If that nominal range is six dB wide or wider, only those frames with energies within it are used. Otherwise, the passing range is widened to six dB symmetrically about the center of the nominal passing energy range. This guarantees that the algorithm will see at least six dB of dynamic range in the speech data, but eliminates regions of probable degradation due to extreme clipping and/or noise, if sufficient dynamic range is present in the original data.
In blind deconvolution, channel effects are then minimized by deconvolving the spectral data to a white spectrum. This is done by multiplying the values in each filterbank channel by a value chosen to make the mean over selected frames equal to a constant. Finally, the fourteen filterbank values are renormalized using the 12 norm. Both reference speech data for target talkers and unknown speech to be classified are subjected to the processing described above, one message at a time.
In message match scoring, the Euclidean squared metric is often used heuristically, without very serious consideration. For example, classical textbooks on pattern recognition rationalize it for nearest neighbor classification procedures by showing a relationship between the Parzen estimate of local probability density and nearest neighbor distance. It is shown that nearest neighbor classification is equivalent to estimating the local density of each pattern class by a Parzen estimate and assigning the unknown to the class with the largest local density, as is required for a Bayesian decision (in the case of equal priors and equal costs) Generalizing to the case where many observations (assumed independent) are to be combined to reach a decision, the paradigm would be to estimate the likelihood ratio in terms of distances and accumulate those estimates. Again following the textbooks, the Parzen estimate of the local density of a reference class at the observation point of the unknown is: ##EQU3## where Vn (x) is the volume of a sphere of radius x in the n-dimensional parameter space, and dNN is the distance f rom the observation to its nearest neighbor in the reference class. Since Vn (x) is proportional to xn, that implies that to accumulate log likelihood ratios we should accumulate values proportional to:
-log(pR)=n log(dNN)
whereas in fact we are accumulating (in the averaging process) values proportional to dNN 2. The reason for this is that the square of the nearest neighbor distance correlates better with the logarithm of the local density than does the logarithm of that distance, as the Parzen estimate would seen to indicate. This is somewhat surprising in view of the robustness and desirable properties of the Parzen estimate touted by some authors. It is easy to show, however, that in a parameter space of more than about five dimensions, an absolutely enormous reference set is necessary in order for the Parzen estimate to be a reasonable one.
In the present system, the parameter space has thirteen dimensions and the reference data consists of at most several thousand frames per message. Under these conditions, in populations which can be approximated as a roughly disjoint union of multi-variate whitened Gaussians with similar variances, the square of the nearest neighbor distance not only correlates well with the logarithm of the local density, but the two are also approximately related by the affine transformation:
-log(pR (u))=a+bdNN 2
for some constants a and b. As the additive constant a disappears when log densities are subtracted to form log likelihood ratios, leaving 12 as a simple scale factor, it follows that the square of nearest neighbor distances is the appropriate value to accumulate to approximate likelihood ratio scoring. The whitening of the Olano parameters mentioned above (whereby the variances in each parameter space dimension are made equal) helps make this approximation reliable. Recognizing this as the basis for accumulating the square of the nearest neighbor distances, the following relations are given: ##EQU4## where pR (u) is the probability density of the reference message at u, pU (r) is the probability density of the unknown message at r, and the approximation now includes the affine shift and scale effects of a and b.
If frames were independent events, or if all messages were of the same length, it would be appropriate to accumulate, rather than average, the du,R and dr,U to form the Score values, as that would be equivalent to accumulating log likelihood ratios. Instead, the average is used because it normalizes the message length and reduces sensitivity to the lack of independence between frames. The Score values are thus appoximations of logarithms of conditional probabilities: ##EQU5## where p(U,R) is thought of as the probability that unknown message U and reference message R were spoken by the same talker. In view of the approximate affine relationship of the Score values to the conditional probabilities exhibited above, summing them produces: ##EQU6## where a' and b' are related to a and b in an obvious way. This is the meaning of the match scores before they are modified by score normalization.
FIG. 3 is a schematic diagram illustrating the message matching and scoring process. Match scores are computed for a combination of unknown and reference messages in two steps. Only the frames selected as described above are considered. First, each frame of the unknown is compared to every frame of the reference message and given a score equal to the minimum Euclidean squared distance between the unknown and all reference frames. ScoreA is set equal to the average of these values over all frames of the unknown message. Next, the same process is repeated, but comparing each frame of the reference to all frames of the unknown, to form ScoreB. The match score assigned to the comparison of this combination of unknown and reference messages is then the sum of ScoreA and ScoreB. Stated mathematically, this process is summarized as follows. If we let uj be the j-th frame of unknown message U and ri be the i-th frame of reference message R, the "distance" from uj to the reference message is: ##EQU7## and the "distance" from ri to the unknown message U is: ##EQU8## The resulting scores are: ##EQU9##
The remaining processing operates on scores found by "matching" the messages in the message queue against a set of reference messages. The reference message set consists of training data for target speakers. In experiments conducted, the reference message set included three 30-second messages from each of 25 target talkers.
It is clear that scores computed as described above are rich in speaker dependent information when the underlying speech feature distributions are themselves speaker-dependent. The presence of P(R) and P(U) in the expression above also indicates that proper interpretation and use of the scores has to take into consideration speaker a priori probabilities and unconditional message probabilities, which justifies the need for further normalization.
The speaker scoring algorithm incorporates three especially important normalization procedures: scoring relative to a representative set of talkers; normalizations with respect to unconditional message probability; and removal of biases due to inference data variability. All three are believed important in obtaining scores which are meaningful across both talkers and messages, which is crucial for both reliable assignment of threshold values in terms of ROC performance curves and for accurate message sorting. The first two normalizations are obtained using reference data for a representative set of talkers, which may often include data for non-target speakers. The third normalization is achieved by using z scores.
Referring to FIG. 4, normalization of raw message-to-message match scores with respect to an entire set of reference talkers, which is accomplished by forcing the rows of the raw score matrix to zero, normalizes scores with respect to the unconditional probability of the message. An atypical queued message will be characterized by unusually large raw scores against all reference messages, i.e., all across its row of raw scores, and typical messages will have smaller raw scores on the average. Rational comparison of one message's likelihood of having been spoken by a target talker to that of another message thus requires such a normalization, and it is done by de-meaning each row. It is critical, however, that this normalization be done with respect to a large enough sample of reference talkers to avoid accidentally destroying information in the raw scores.
Use of the negative z score, which normalizes scores with inspect to the variance of scores over the message queue, takes into account the observed fact that reference messages usually have an inherent bias which manifests itself as a propensity toward larger or smaller score values against all messages, whether by the same talker or not. Rationalization of the z score can be understood in terms of likelihood ratios as follows. Raw scores (before row, or reference message, normalization) are non-negative measures of dissimilarity between the reference message and the unknown message. The conditional probability densities of this measure,, given that reference and unknown were spoken by the same or different talkers, characteristically rise smoothly from zero to a single mode and fall smoothly to zero. The central tendency of the same-talker case is much smaller than that of the different-talker case, i.e., the same talker scores tend to be much smaller than the different-talker case. As a result the log likelihood ratio falls monotonically from a high positive value at the left through zero to very negative -values at large score values. Approximating this behavior with a straight line shows that it is like the negative z score of the different-talker probability density, up to an additive constant. The biases inherent in reference messages manifest themselves in these densities primarily as a change in scale, affecting the same-talker and different-talker distributions in essentially the same way. The z score of the different-talker probability density is invariant with respect to such scale differences. Therefore, using it as an approximation to the likelihood function automatically removes any effect of the reference message scale bias. In implementation, it is assumed that the same-talker case arises rarely so the mean and variance of the scores for a given reference message are essentially those for the different-talker case, and can safely be used to compute the negative z scores.
The rationalization given above uses many approximations and assumptions. However, the excellent performance of this baseline algorithm in sorting queues strongly indicates that the normalizations used are successful in producing scores monotonically related to negative logarithms of the Bayes ratio (the probability of the queued message given that it was spoken by the hypothesized talker, divided by the unconditional probability of the queued message), at least for the range of compositions of queues used in the tests. At present this scoring computation appears to be justified primarily on the basis of its success in speaker spotting.
In implementation, match scores are computed for each message in the queue against each reference message, as shown in FIG. 4. Treating this set of match scores as a matrix, one column per reference message and one row per message in the queue, the scores are first normalized across rows by subtracting the constant for each row which reduces its mean to zero. This is the very important normalization of the scores with respect to target and reference talkers. Scores are then normalized within columns, by replacing each with its negative z score relative to the column mean and variance.
These two normalizations can be expressed mathematically as follows. Let the original scores in the (Message queue)x(Reference messages) matrix be denoted dm,r. Then:
dm,r =ScoreA (Um,Rr)+ScoreB (Um,Rr)
The result of normalization with respect to the reference set is replacement of dm,r by d'm,r, where ##EQU10## and the final score for each message in the queue and each reference message is: ##EQU11## where <d,m,r 22 m is the mean of the score column for reference message r and varm (d,m,r) is the variance of the scores in that column.
Queues are sorted on the basis of a set of z scores for each message in the queue and each target talker. Each target talker is given a match score against each message in the queue equal to the average of all normalized match scores for his reference data against that message. (It is common to represent a target talker by all, or at least several, reference messages taken from his training data.) This produces a matrix of scores of each queue message against each target talker.
Queues are then sorted as follows. For a single target talker, the queue is ordered on descending values of the (single) final score value, larger scores indicating greater posterior probability of the talker. When the target talker set contains more than one talker, each queue message is given a target set score by treating the message/talker scores as proportional to the logarithm of the Bayes ratio, i.e., the conditional probability of the unknown message, given the talker, divided by the unconditional probability of the message. The score given to each message for the whole target talker set is then: ##EQU12## where T is the set of target talkers, Sm,t is the message/talker score for message a and talker t, and pt is the a priori probability that talker t is in the queue. The queue is sorted on descending values of this sum, with larger values indicating greater likelihood that a message is from one of the target talkers.
The performance of the baseline algorithm for message sorting was tested using a control database (referred to as the King database). The King database consisted of recordings of 50 male speakers engaged in speech-elicitation tasks designed to simulate ordinary telephone conversations. Each speaker had ten sessions of roughly 40 seconds duration. A telephone "loop-back" arrangement was used, wherein subjects placed calls through a long-distance carrier to a separate telephone line at their same location. In this way, simultaneous recordings were made of the direct signal using a high-quality microphone and of the signal received at the "far end" of the telephone line.
Because speaker sorting is an "open set" problem, a set of non-target speakers who are not used to train the algorithm were set aside for testing. The database was divided into 25 even-numbered speakers, used as targets, and 25 odd-numbered speakers, used as non-targets. The simulated message-queue was composed of one message from each of the 50 talkers. The reference set was composed of all the training messages for the target talkers. Histograms of scores were generated for targets and non-targets. These, in turn, were used to generate ROC curves which measure performance obtained using the same threshold for every target.
FIG. 5 shows measured ROC curves for three conditions: the curve marked "Clean" represents training and testing of the clean, or wideband, database; the curve marked "Telephone" represents training and testing of the telephone, or narrow band, database; and the curve marked "Across the Great Divide" represents training and testing on a telephone database including differences between first and last sessions probably caused by changes in the recording equipment.
These results show some of the best performance that have been reported on the King database. At a detection rate of 80%, no false alarms were observed when using clean speech, or when training and testing on the same side of the "great divide". When testing across the "great divide", the false alarm rate was 0.8%.
Table I below presents performance of the baseline algorithm on closed-set speaker identification. The test was restricted to the first 16 talkers of the King database for comparability with recent published results on the same telephone data. The performance measures given are the average rank of the true speaker and the percentage correct identification accuracy. These measures are shown for both "raw" scores, without applying the score normalization process, and normalized scores.
TABLE I______________________________________Baseline Algorithm Closed-Set Performance Score Average PercentTrain Test Norm. Rank Correct______________________________________1, 2, 3 4, 5 No 1.03 971, 2, 3 9, 10 No 1.41 661, 2, 3 4, 5 Yes 1.0 1001, 2, 3 9, 10 Yes 1.0 100______________________________________
The excellent performance of the baseline algorithm is obtained at the cost of a large computational burden. However, it is feasible to implement this algorithm using available processors. For estimation purposes, all the computation may be attributed to the inter-message matching process. In the worst case, this process compares all frames of all messages in the queue with all frames of all the reference messages. If both the message queue and the reference set contain 75 messages of 2000 frames (40 seconds) each, then (2000�75)2, or 2�1010 distance measures, or roughly 5�1011 operations are required. These computations are done using fixed-point arithmetic, representing parameter values with 8 bits precision. Squares may be found using lookup tables. To process the 50-minute queue in 50 minutes would require computation at a rate of about 160 MIPS. In fact, the actual required computation is much less than this because the speech frame selection step eliminates many of the frames (e.g., about 80% of the frames in the King telephone database) from entering the computation. In addition, the computation consists of massive repetition of simple operations, which is ideal for implementation using parallel processing.
A union event occurs when any of a specified set of talkers engages in conversation. An intersection event occurs when two specified talkers both engage in the same conversation. For the split channel case, the definition of an intersection requires that there be an external indication that two messages belong to the same conversation.
The probability of a union event should be the sum of the individual probabilities of the talkers in the set. Given that only one or at most two of them can actually be engaged in the conversation, the sum reduces to the best talker's probability, or the sum of the two best talker's probabilities. To a first approximation, the score for the union event is taken as the best score for any talker in the union set.
The probability of an intersection event should be the product of the individual probabilities of the two talkers, assuming the activity of the talkers is independent, Since the scores of the baseline algorithm correspond to logarithmic probabilities, the score for the intersection event is computed as the sum of these scores for the talkers comprising the event.
The co-channel regime is characterized by an input audio signal which is a two-sided conversation without external indication of which segments of speech are generated by either talker alone or by both talkers simultaneously. To correct for this, the algorithm designed for the co-channel problem can model the timing patterns of telephone conversations between two speakers. One fairly simple extension of the baseline algorithm demonstrates the feasibility of sorting on co-channel signals. The method relies on models that explicitly allow for the presence of two talkers inter-mixed. During each short period of speech (200 ms), assumed to be purely a single talker, a score, Ri, for every reference talker i is accumulated. At the end of each 200 ms period, all joint hypotheses, Mij, are updated by accumulating the better score, min (Ri,Rj), from the two talkers, i and j, represented in that specific joint hypothesis. All mixed hypotheses are accumulated from throughout the whole conversation. After accumulation in the space of logarithmic probabilities, the hypothesis likelihoods are exponentiated back into a scaled probability space.
Scores for joint and single talker hypotheses are simple applications of Bayes rules. If a certain pair of speakers, a and b, was the target event, then that specific joint hypothesis Mab, normalized by the probability of all joint hypotheses, SUM Mij, would be an appropriate probability measure with which to score that pair. ##EQU13##
If the sorting key is a single target talker, t, all messages are examined where t talks with any unspecified other talker. The unspecified talker is assumed to be represented among the reference talkers, with uniform probability 1/N, where N is the number of reference talkers. The score for talker t is the average of all joint scorns in which he participates. ##EQU14##
To visualize, when the target talker speaks, the matrix of joint hypothesis likelihoods shows a good row and column intersecting on that target talker. When an unmodeled talker speaks, the matrix of likelihoods is relatively flat. Summing across the row or column correponding to the target talker tends to give a better score when the target is speaking.
FIG. 6 shows the measured performance for single-talker sorting keys on simulated co-channel data using a version of the recognition algorithm, compared with the split-channel whole-session performance using the same algorithm. Under these benign testing conditions, the simulated co-channel data gives 6% probability of false alarm at 80% probability of detection.
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One hundred forty-five seniors graduated from Shelley High School on Tuesday.
Jessica Johnson and Willson Higham were the co-valedictorians.
Kayla Butler was named Miss Shelley High; Will Higham was named Mr. Shelley High.
To date, the class of 2012 has garnered $706,436 in scholarships.
In her valedictorian address, Johnson gave a brief overview of the students' years together.
ŌĆ£In sixth grade, we learned to say ŌĆśpoo-pooŌĆÖ instead of crap,ŌĆØ she said.
Kindergarten students from Blackfoot Community Charter Learning Center (BCCLC) gave a smashing rendition of the age-old tale "Goldielocks and the Three Bears".
Students sang their little hearts out to catchy tunes that told of cold porridge, broken chairs, and the important message of not "snooping".
The warning signs of abuse in a teen dating relationship aren't hard to spot in this high tech age that we live in but they are important for everyone to know.
A free presentation on abuse in teen dating relationships was held at Bingham Memorial Hospital Monday with attorney Fred Zundel of Idaho Legal Aid Services in Pocatello and Dixie Chapman, executive director of Bingham Crisis Center the hosts/
"The key is to catch the dynamic of domestic abuse early and rectify it," Zundel said. "We need to stop the abuse in early ages before a guy has spent 10 to 15 years pounding on a woman."


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Leverage your Hispanic heritage to seize unseen opportunities.
CHL's interactive training and virtual classroom was designed to empower, expand and accelerate the influence of Hispanics as leaders, small business owners and consumers. Whether you're looking to develop your leadership skills and advance your career, lead your Hispanic employees, or sell to your Hispanic customers - experience the opportunity to unleash talent and propel your business forward.


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McCarthy Hotels
Mccarthy Lodge & The Ma Johnsons Hotel
101 Kennicott Avenue, Mccarthy, AK, 99588
Satisfaction: Excellent
Good for: Business
Kennicott River Lodge
Mile 59, McCarthy Road, McCarthy, Alaska, United States
Satisfaction: Excellent
Good for: Couples
McCarthy Local Customs
The amenities are not much to speak of, but it has a million dollar view.
McCarthy has year-round residents, but its summer population swells by ten times or more. I suppose a ramshackle cabin like this might work as a summer residence (though it doesn't look like it would do much to keep the biting flies and mosquitos out) but certainly this place would not be much use in the -40 degree temps in the dead of winter.
You see shacks like this all around rural Alaska.Related to:
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Favorite thing: I was born and raised in Alaska. McCarthy is the best place in the state to visit. Pristine wilderness, wildlife, amazing bike trails, no cars......other then the stray Mccarthyite's subaru (long story), Kennicot Mine, and the largest standing bar mirror in the state.
Great 4th of July festival. Drive in, fly in, camp, hotel, no star through 4 star accomodations, bike rentals, a pizza shop, and in the 90's you could bungee jump off the McCarthy bridge.
Fondest memory: Took my wife there on our first "overnight" trip. She bungee jumped off the bridge, rode mountain bikes to Kennicot, and I think our daughter was conceaved there.
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Pearson 26 bulkhead delamination
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Re: Pearson 26 bulkhead delamination
If this is NOT the main bulkhead then the problem is minor and the bulkhead is easily to replace and re-tabbed in with filled epoxy and cloth. Just angle-grinder away all the years accumulation of paint and other debris and 'rebuild'.
HOWEVER if this is the main bulkhead ... the 'compression post' and main bulkhead are INTEGRAL then you might have second thoughts about the boat, especially if the bulkhead has shifted ..... and the 'head door' does not close without binding !!!!!!!!
If the main bulkhead has shifted, the companionway door hardware (door passageway knob, etc.) will no longer 'match-up'. What happens to the compression post/bulkhead combo is .... When Pearson installed the C-post and main bulkhead into the boat, they drove a wooden wedge under the base of the C-post (in the bilge !!!!) and simply 'tabbed it over' with fiberglass to hold the wedge in place. Being in the bilge and after all these years the wedge rots, the rot grows into the base of the post, the compression into the bulkhead 'relieves' (the bulkhead takes all the stress of MAST and coachroof, not 'just' the compression post) ... and the bulkhead shifts out of place.
Look carefully at the head door and does it close without 'binding' and does the door hardware line up reasonably, then look in the bilge and push a small screwdriver into the tabbing to look to see if that wedge is still there, Also inspect the BASE of the Compression post .... if its rotting it will show darkening on the outside surface. Just about ALL pearsons of the early 70s used a wedge under the compression post in the bilge .... replace it with one made out of fiberglass, then 'tab' it in place to hold it there.
If the C-post IS rotted but the bulkhead is not shifted ... its an easy repair --- just cut the bottom of the post off, make up a solid FRG 'pedestal' base, jack up the 'coachroof' and slip in the new base, etc. ... prevents the inevitable Pearson bulkhead 'shifting'.
Rudder bearings, etc. are still available from drmarine.com ... they are delrin and seriously abrade when the rudder shafting becomes roughened over time. Dig a pit under the rudder, drop the rudder and do a 'complete job' when replacing the bearing .... polish the shaft too !!!!
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MD Theatre Guide by MD Theatre Guide
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Maryland Theatre Guide is your ticket to theatre news in Maryland, DC, and Northern VA! MD Theatre Guide's mission is to provide reviews and articles that are fair and honest, thorough, and family friendly.The "Theatre Guide" section lists theatres by city/county along with their website. If you are in the theatre industry, then check out our "Auditions" page where we list auditions and opportunities.
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What should be a depressing story about yet another employee of the Catholic Church being fired for being gay actually has an uplifting twist: the youth community rallying in support of same-sex marriage.
In a refreshing sign of the changing times, teenagers in Washington walked out of school to protest discrimination against the termination of their vice principal. We're not just talking the Gay-Straight Alliance either – hundreds of students participated, nearly the entire student body, along with some faculty members, as well.
Mark Zmuda, an administration at Eastside Catholic High School, married his husband this past summer, but it wasn't until school officials learned of this fact a couple of weeks ago that he encountered any problems. Though there's some argument over whether Zmuda was fired or forced to resign, one thing is certain: one way or another, Zmuda would not be able to keep his job because of his sexual orientation.
Fortunately, there's no need to tell residents of Eastside how bogus this situation is. The youth community has rebelled to make it clear that it won't stand for the discrimination and intolerance. Using Twitter and other forms of social media, students organized both a sit-in and walk-out to demonstrate their support for Zmuda. Simultaneously, students at other local Catholic schools staged similar protests to show their solidarity.
According to students, Zmuda's sexuality did not influence his job performance. "We did not know he was gay before today," said Christian Leider, a senior. "He's always looking out for the best in everyone and he always wants everyone to do their best."
The fact that most of the students identify as Catholic is also significant. Though church officials still preach an adherence to "traditional" morality, the Catholic population is growing increasingly liberal.
"Just because I'm Catholic doesn't mean I need to believe every rule the church has," said freshman Sophia Cerino. "We think the rule over gay marriage is totally unfair. Everyone seems to think the same thing — that we should all be treated equal."
When speaking to the media, many Eastside students referenced the Pope's views on homosexuality. Earlier this year, Pope Francis indicated that the Catholic Church puts too much emphasis on condemning homosexuality and that other moral issues warranted more attention instead.
Even with the support of Catholic youth – and perhaps Pope Francis – it doesn't seem likely that Zmuda will win his job back. The order for Zmuda's termination came down from the Archdiocese of Seattle. According to lawyers for the church, Zmuda broke his employment contract that forbids same-sex marriage.
Whether or not he regains his position, Zmuda will have a lifelong impact on his students. Addressing the student body at a rally in his honor, Zmuda said, "My personal life is a small part of who I am. I am first a teacher and administrator and I try my best to be an educator to each and every one of you. I hope that no matter what happens to me and to the school, that all of you guys will seek a career and work very hard, find the love of your life, hopefully one day get married as well. At the same time, no matter what happens, strive to do your best."
Read more: activism, catholic church, Eastside Catholic High School, education, gay, gay rights, high school, homosexuality, lgbt, Mark Zmuda, pope, pope francis, same-sex marriage, school, teenagers, vice principal, youth activism
Photo Credit: Eric Jensen
Thank you.
Thank you
A contract is a legally binding document so they were well within their rights to fire him. However, it is good to see the students showing tolerance, loyalty and support for him even though he is legally in the wrong for breaking his contract - not for his lifestyle choices.
good for them
Mutiny....THANKS FOR THE GOOD NEWS!
Great job kids!! Remember to keep it peaceful and legal, okay? Otherwise you put your vice-principal in a situation even more uncomfortable than the one he's already in. Make him proud.
Hurray for these children for doing the good thing, the right thing, the hard thing. They will learn a lot from this, both good and bad.
Disclaimer: The views expressed above are solely those of the author and may
not reflect those of
Care2, Inc., its employees or advertisers.
It's about being your own advocate, choosing your doctors wisely and clear communication. I make it…
What a fantastic video!! To think that that magnificent creature had never been allowed to run or feel…


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Great beds
posted 6 years ago
I love these beds. They are used at the shelter, and it's very easy to clean them. There are few dogs who don't like to sleep on them. It's hard to chew them up, and it's next to impossible to get at the fabric to chew that up. They save on laundry, too! These are fabulous!
Pros: very easy to clean
Cons: not much
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Money saver
posted 6 years ago
These beds have saved me alot of money. Our dane was chewing every style of bed we put in his kennel. We've now gone on 7 months w/ the Kuranda bed and can't find any damage. We have an extra in the living room and all the dogs have been seen laying on it.
Pros: easy cleaning, chew proof
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Great product!
posted 6 years ago
We love these beds at the shelter where I work. They are easy to put together and easy to clean. Since we don't have to use as many blankets, these beds have helped cut down on the laundry too. Nice colors and reasonably priced.
Pros: Easy to clean
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posted 7 years ago
I have recently donated two beds to our local shelter for the dogs and I am told they love them. These beds are very durable and they keep the dogs off the cold cement floor. The beds are very reasonably priced.
Pros: Keeps dogs off cold floor
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Caolán McNamara wrote:
On Tue, 2009-07-14 at 10:21 +0200, Daniel Mach wrote:Release doesn't contain a %dist tag. - %dist is missing in Release: in spec- http://fedoraproject.org/wiki/Packaging/NamingGuidelines#Using_the_.25.7B.3Fdist.7D_Tag"If you wish", so until now it hasn't been an error to not have one. Has this changed ?
Nothing changed. See my reply to Jakub's email, please.
That's a good point, I was never thinking about post-release snapshot packages, always just about pre-release ones.Pre-release build's release should start with '0.'.- a pre-release build is detected with these patterns: "test", "alpha", "beta", "rc", "[\.0-9]a[\.0-9]", "b[0-9]+", "snap", "pre", "dev", "build", "bzr", "cvs", "svn", "git", "hg", "trunk", "branch" - release should start with .0, but generally it can start with any number as long as it's bumped before each build.But of course we also have post-release snapshot packages, so if alliance-5.0-26.20070718snap is a svn/cvs snapshot *post* 5.0 release then its be perfectly correct, right ? C.
Thanks for catching that. - daniel -- Daniel Mach <dmach redhat com> Release Engineering, Red Hat


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(2 years ago)
Administration is extremely rude. My daughter was bullied by other kids and no one took my complaints seriously. Former principal was good solid educator. The new one I am not sure about because in almost 3 months that my child is back to school I have never seen her there. Teachers twist everything around and blow things out of proportions. One female volunteer is always rude and acts as if she is a principal there (if I knew her name would definitely post it here). She harasses parents yells and disrespects the children. Next year my child definitely not returning there. Not a good or safe environment to be at. Shame on administration.


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When do you call it quits?
View Single Post
Joined: Apr 2005
Oddometer: 57
Calling it quits....
Originally Posted by hapbob
This question is often thought about I am sure and When I decided to ponder my own future with Motorcycles I noticed I have Changed not quit. I have had a bike since I was 14 yrs old and I'm now 57. I have had about every bike, Road ,dirt ,ect. Harleys, Goldwings, Bmws ( yes all my retirement money was spent on bikes). I now ride often but they are short trips and errends. 100 miles is a big day. I now own a scooter that does eveything I need it to do. A honda Helix that goes and goes and I'm both comfortable and confident with it. No worrries of slow u-turn,gravel on corners and it's not $20K worth of machine sitting around to much. All my big road bikes were becomming to heavy and expensive and wasn't riding them like they were ment to be ridden. One bike/scooter I just bought is a Trike...a Big Scooter 650 Burgman trike...Going to see if I have desire to go farther with it than just around the neighborhood. Just bought it and Need to wait for June for weather and time to ride. So I am not afraid to ride..but have evolved to a close to home rider...and a low cost easy to handle bike/scooter/trike This Trike is more fun than you would think!!!!!!!!!!
Funny reading this... Man and his circumstances. You never really quit riding motorcycles. I have followed the sport for 30 years. Over that period of time, I have departed from riding for similar reasons and returned to the sport . Good to see you are still enjoying the riding, especially in the the summer months. The big bikes in the garage require more time to ride. 1500 mile trips is when the big bike shines. The scooter is a blast however, as others on this forum have well pointed out, there is no true substitute. The cars are the problem. The folks out there driving don't see a motorcycle on the road. Although riding in the neighborhood seems safe and friendly, I opted to ride single track/off road. Unless a mountain lion gets me, all should be OK...
I am delighted to find yet another way of riding a two wheeled machine.
All the best
bimotakid screwed with this post 01-11-2013 at
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CAIM/Amazon Activate Program – Apply Here!
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The Center for Advanced Information Management at Columbia University helps companies achieve technical and economic success from their products and processes. We strive to provide effective, targeted assistance and to utilize the university's intellectual strength and comprehensive infrastructure. We offer flexible approaches to solving industry's problems. Our goal is to enable our industry partners to grow and prosper and to commercialize Columbia-developed technologies.
The Center for Advanced Information Management teamed up with the illustrators and
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|Separate WC's
|Property type
|Minimum Stay
|3 - 7 nights
Vista Dulce, our luxury condominium RIGHT ON THE BEACH, has been recently updated with new state of the art A/V and high speed Internet equipment, painting and furnishings. Our home is conveniently located only 2.25 miles north of the town of San Miguel where shopping, dining, sight seeing and many recreational activities are available. There is a pier on shoreline that offers a dock for your boats and family fun. Vista Dulce's most appealing features are its wide terraces and breathtaking views. Vista Dulces' amenities include a freshwater heated pool with fresh water shower. Vista Dulce offers daily maid service, washer/dryer, purified water (yes, you can drink it), FREE LONG distance calling to US, Canada, Mexico & 60 other countries.
Spectacular Sunsets, Terraces, Views: Vista Dulce's most appealing features are it's wide terraces and breathtaking views. Vista Dulce has a comfortably furnished 65-foot oceanfront terrace with unobstructed views of the coastline and exotic reef formations. This spectacular view offers fabulous Cozumel sunsets. There is also a smaller balcony equipped with a gas grill overlooking the luscious green of the Cozumel's inner jungle just off the dining room. You can choose to relax on the terrace with its cushioned patio chairs and tables or sway in genuine Mexican hammocks.
Take advantage of the many other options that Vista Dulce offers such as our shopping service or in-house cook service featuring delicious flavors of the Mexican and Mayan culture. We also offer baby-sitting services, car rental options, many exotic tours and unusual excursions. You will feel truly pampered in Vista Dulce, your perfect Home away from Home for your Cozumel vacation.
Immaculately furnished with fine art and leather, imported objects with touches of local accents give this home a feeling of Mexican luxury living. Beautifully Italian and Mexican handcrafted furniture of mahogany, leather, and other fine woods create a luxurious setting. The walls come alive with colorful fine art prints and other hand made works including Talavera pottery and comfortable rugs and mats in all rooms.
Our home's generous amenities include the following: a striking freshwater heated pool with outdoor poolside bars, a fresh water shower; included is daily maid service (except Sundays), washer/dryer, purified tap water (yes, you can drink it), free long distance to US, Mexico, Canada & 60 other countries, local telephone service, answering machine, wireless super high-speed Cable Internet access and Dish Network satellite TV with American and NY Network channels. We also have 100's of CD's and DVDs. World class snorkeling and diving on the Cozumel's famous reefs are available at your doorstep. Just walk right out your door onto the beach and dive into the Caribbean.
We got tired of staying in places that were poorly furnished and uncomfortable. We wanted a place that felt like home, so we furnished Vista Dulce for ourselves. We also wanted access to all the latest technologies, so we could live there if we decided to. It was only afterwards that we thought about renting out our home. We take maintaining Vista Dulce in top condition very seriously, so you will feel right at home.
Janey and Ledge, Denver.
Ledge Brady purchased this condo in 2002
We wanted a place that was on a white sand Caribbean beach, within 4 hours of Denver and a place that never got cold in the winter. After searching Florida, Puerto Rico, BVI's and the mainland of Mexico, we kept coming back to Cozumel. When we first walked in and saw only blue sky and turquoise waters, it took our breath away; hence the name, Vista Dulce (Sweet View).
Vista Dulce is quiet and takes up an entire floor, so we have no one on either side making noise. You walk right out onto the terrace that overhangs the beach and the Caribbean. It is private but only a few minutes to town.
Hi Ledge,
We had a wonderful time in Cozumel. You have a lovely condo. Everything was extremely organized – could not have asked for anything else. The User Guide is just super…so much useful information. Spot on regarding restaurant recommendations…except Otates…we didn't like the fact that they returned the unused condiments (salsa, onions) to the main container and use it for other diners. Loved El Moro and La Choza…San Carlos had a great fried fish…
Hope to be back next year.
Just returned from a 7 night stay at Vista Dulce. My wife and 3 daughters (21,20,16) were along also. We dove 4 days with Albatross Charters (excellent small outfit). The condo is great and the location can't be beat. This is much better than the house we rented before that was in town. If you're going to Cozumel,you need to stay on the beach, if you can and this is the place. The views, amenities, and layout are outstanding. Thanks for a great week!
This is a fabulous place to stay on Cozumel Island! Two couples shared the condo, and we loved that we each had a wonderful master bedroom, as well as all the amenities. We rented bicycles from town, and that was our major form of transportation. We found we could get anywhere we needed to go, and get some exercise while doing it. It is a nice walk to town as well. We found a dive shop close by (Pepe Scuba), and went on several dives. They were great also, and we were able to store our gear with them during the week. We loved going out to restaurants in the evening, but ate breakfast and lunch at the condo. All in all, a wonderful time. I'm writing this several years later, (we stayed in 2011) and I still remember this place fondly.
Vista Dulce is a great place to stay. The home is top notch. We have been staying in HomeAway rentals for the last 10 years or so and this is one of the best places we have ever stayed. The rooms are large, the home is very clean, and the Amenities are too numerous to mention. I have two teenage daughters and they both loved being in the condo. After a day out and about in Cozumel, it was just wonderful to have a place with a pool, right on the beach to kick back, relax drink a beer and enjoy life. Thank you Ledge and thanks to all the people at Las Brisas that made this a very memorable trip.
Vista Dulce offers luxurious accomodations right on a beautiful beach in Cozumel close to town but away from the crowds. We hosted our adult children and their partners for a week and the condo offered plenty of space for everyone to spend time together and also lots of opportunities for solitude. The kitchen is very well stocked and the daily housekeeping service is wonderful. Would recommend to anyone!
We stayed here for a week over Easter - my husband and I, daughter and son-in-law, three grandchildren. This condo is absolutely beautiful and well equipped. I could have probably spent the entire week out on that balcony overlooking the ocean and been quite content. It is spacious and airy. There is a housekeeper who comes in six days a week to clean and she is the best, you could eat off the floor by the time she is done. We loved it. We enjoyed Cozumel Island, we haven't been here in quite a few years. Our favorite restaurant is still here, El Moro, highly recommend, we went two times. We enjoyed a day at Paradise Beach.
We have one more day at Vista Dulce, so I thought we would write our review (while having a cerveza) now instead of trying to remember when we head home.First off the owner gave us a great tip for the airport. If traveling light walk a bit out of the airport to catch a cab. Great tip it use it.
If you are looking for a home away from home this is the place. We have been here for 7 nights and have enjoyed every minute. The kitchen is stocked, furniture is clean and in good condition. We have eaten breakfast and lunch on the terrace everyday. We have eaten all our meals in as my wife has a gluten allergy and makes it difficult to eat out. Saved us a good amount of $$$.
Walked several times to town to check out the local seen. It is about a 45 minute walk, but has a great walking path all the way there. We did walk throughout the marina on the way to town several times as well to check out the boats with the kids. Pool and beach at the condo are great. The pool is heated and that is not normal in this area. Many diving options but are kids are not old enough to dive just yet.
Vista Dulce folks have been great to work with. They have a great visitor guide that I highly recommend you go through it to help guide you through Cozumel. Helped us navigate as well as what to expect it probably saved us some money by having a little local insight into the area.
We did three of the beach clubs and they were great for both us and the kids. Playa Mia was one Paradise Beach and Buccanos. Would definitely recommend any of them. Our reviews were the following;
Paradise beach - less expensive then Playa Mia, better food not as many beach toys as Playa Mia
Playa Mia - better toys at beach, buffet..good for the drinks but the food was just so so
Buccanos (by the condo) - great place to hang out. However during our visit the jellyfish rolled in and were stinging all the snorkelers. Surprisingly, the staff told us this before we got in the water and when we decided to leave gave our entrance fee money back.
Only small negative was the time the maid came. She usually showed up by 2 or 3 in the afternoon and the kids were usually done with the beach by the and we just wanted to hang at the condo and chill out. We dealt with this situation by walking into town at that time to not interrupt her. Again, not a huge deal, but would be great to have a bit earlier. But you have a maid for cripes sake
Thanks for a great trip and we will be back when both kids can go diving
World Class Scuba Diving and snorkeling, zip lining, horseback rides
|Rental Period
|Minimum Price
|Additional Nights
|Weekend Night
|Monthly *
|Event Price
Summer 2015
19 Apr 2015 - 14 Aug 2015
4 night minimum stay
for 4 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
Low Season 1 2015
15 Aug 2015 - 20 Nov 2015
3 night minimum stay
for 3 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
Ironman/Thanksgiving 2015
21 Nov 2015 - 4 Dec 2015
Changeover Day: Saturday
7 night minimum stay
for 7 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
Low Season 2 2015
5 Dec 2015 - 18 Dec 2015
Changeover Day: Saturday
4 night minimum stay
for 4 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
Christmas 2015
19 Dec 2015 - 25 Dec 2015
Changeover Day: Saturday
7 night minimum stay
for 7 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
New Years 2016
26 Dec 2015 - 1 Jan 2016
Changeover Day: Saturday
7 night minimum stay
for 7 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
High Season 2016
2 Jan 2016 - 22 Apr 2016
Changeover Day: Saturday
7 night minimum stay
for 7 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
Summer 2016
23 Apr 2016 - 2 Sep 2016
4 night minimum stay
for 4 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
My Standard Rate
Changeover Day: Saturday
7 night minimum stay
for 7 nights
|Notes: Rates are based on 4 guests; add $30 per night, per additional guest
|Pet Fee
|$50.00 + Tax
|Tax Rate
|All rates are for up to 4 guests. For every guest over 4 add $25/nt plus tax per guest to the base rate. Tax rate is 19%. All payments are non-refundable. We recommend that you consider trip insurance.
* Approximate monthly price. Actual pricing will depend on the days of the month you stay.
Please Note: The owner of this property accepts payment in the original currency specified (USD), before use of conversion tool. The conversion tool is provided for your convenience only. The owner may accept other currencies if specifically agreed in advance.


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by Joe Romm Posted on April 11, 2014
Two new studies confirm climate change is drying the U.S. Southwest -- and nearly a third of the world's land faces drying from rising greenhouse gases.
by Mike Rivera Posted on April 8, 2014
Despite a press release saying he is in favor of equal pay, Steve Pearce (R-NM) has either voted against or abstained from voting on any equal pay measures in the House.
by Ari Phillips Posted on January 14, 2014
A new study by the EPA finds there's still time to clean up the huge underground plume before most of the city's drinking water is contaminated.
by Zack Ford Posted on December 19, 2013
The New Mexico Supreme Court has ruled that same-sex marriage is legal, making it the 17th marriage equality state.
by Aviva Shen Posted on December 2, 2013
Carol Lester says the privately-run prison denied her medical care and then stuck her in solitary confinement after she complained about it.
by Kiley Kroh Posted on November 23, 2013
"They have essentially gutted the renewable portfolio standard."
by Tara Culp-Ressler Posted on November 21, 2013
Albuquerque voters rejected a proposed 20-week ban this week, a move that's leading abortion opponents to admit they may need to adjust some of their controversial tactics.
by Tara Culp-Ressler Posted on November 20, 2013
In a closely-watched fight over abortion rights, Albuquerque residents resoundingly rejected a measure that would have outlawed later abortions.
by Tara Culp-Ressler Posted on November 19, 2013
"This is something that's going to impact the whole country," women's health advocates are warning.
All eyes are on New Mexico -- and for good reason. Here's how Albuquerque's abortion fight could end up having huge implications for women across the country.


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The semipalmated sandpiper is considered the most abundant migratory 'peep' and one of the most numerous shorebirds in North America (Peterson 1980; Kale 1990). Detailed abundance records for sandpiper populations within the IRL are scarce. However, birds are known to stopover throughout Florida during spring and fall migrations, and some individuals may winter in Florida Bay (Kale 1990).
- Audubon. 2010. Semipalmated Sandpiper. National Audubon Society. Online at http://web1.audubon.org/science/species/watchlist/profile.php?speciesCode=semsan (Date accessed: 08/10/2010).
- Collazo, JA, O'Harra, DA & CA Kelly. 2002. Accessible habitat for shorebirds: factors influencing its availability and conservation implications. Waterbirds 25: 13-24.
- Farrand Jr., J (Ed.). 1983. The Audubon Society Master Guide to Birding Volume 1: Loons to Sandpipers. Alfred A. Knopf. New York. USA. 447 pp.
- Kale II, HW & DS Maehr. 1990. Florida's Birds. Pineapple Press. Sarasota, FL. USA. 288 pp.
- McCurdy, DG, Forbes, MR & JS Boates. 1999. Evidence that the parasitic nematode Skrjabinoclava manipulates host Corophium behavior to increase transmission to the sandpiper, Calidris pusilla. Behav. Ecol. 19: 351-357.
- Paulson, D. 2005. Shorebirds of North America: A Photographic Guide. Princeton Univ. Press. Princeton, NJ. USA. 361 pp.
- Peterson, RT. 1980. A Field Guide to the Birds: A Completely New Guide to All the Birds of Eastern and Central North America. Houghton Mifflin. Boston, MA. USA. 384 pp.
- Safriel, UN. 1975. On the significance of clutch size in nidifugous birds. Ecology 56: 703-708.
- Schneider, DC & BA Harrington. 1981. Timing of shorebird migration in relation to prey depletion. The Auk 98: 801-811.
- Terres, JK. 1980. The Audubon Society Encyclopedia of North American Birds. Alfred A. Knopf. New York. USA. 1109 pp.
- USFWS. 2010. Migratory Birds and Habitat Programs. U.S. Fish and Wildlife Service. Online at http://www.fws.gov/laws/lawsdigest/migtrea.html (Date accessed: 08/10/2010).
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9 Website Templates and counting!
Online Store 3-color Full Site Website Templates
Are you considering starting up your own on-line store to sell things or provide services? You need a clean and professional looking website to feature your products and services, at least if you want to be successful you do. There are enough On-line Store website templates here to cover just about anything you could want to sell, jewelry, electronics, clothing, movies, music, hosting services, and more. Don't waste days trying to come up with a storefront design on your own, days that could be better spent serving customers. Get yourself a spring board of an On-line Store website templates and jump ahead of the competition today! These website designs are our full site templates that come available in three different colors. Our online store 3 color full site templates come packaged with three different color schemes so that you can choose your favorite, or use multiple colored pages throughout your website. The template designs consist of five or more pages that may or may not have Flash animation. See the more info page on a particular template for details on if it includes Flash. These templates consist of a main page and the necessary content pages corresponding to each of the menu tabs that you see on the screenshots for a template. You do not need to develop any sub-pages because you already have them with the package. The only thing you need to do is to replace sample content with your own.
Page Number: 1
Page Number: 1