text:
|Publication number
|Publication type
|Application number
|Publication date
|Oct 3, 1995
|Filing date
|Feb 8, 1993
|Priority date
|Feb 8, 1993
|Fee status
|Publication number
|Lalit R. Bahl, Peter de Souza, P. S. Gopalakrishnan, Michael A. Picheny
|Original Assignee
|International Business Machines Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (7), Non-Patent Citations (6), Referenced by (33), Classifications (15), Legal Events (4)
|External Links: USPTO, USPTO Assignment, Espacenet
The present invention relates generally to labelling of speech in speech recognition systems. Specifically, the present invention relates to context-dependent labelling of speech.
Phonologists have attempted to find the smallest set of sound units, called phonemes, sufficient to distinguish among different utterances. Each phoneme is represented by a symbol. This symbol is called a phone. For instance, /p/ and /b/ are distinct phonemes of English, because they distinguish such words as pin and bin from each other. However, it should not be thought that acoustic intervals labeled by the same phoneme would necessarily sound alike. The acoustic variants of a given phoneme are called allophones. Different sounds may be allophones of the same phoneme if at least one of two conditions is met that prevents them from distinguishing utterances. Two allophones either never occur in the same sound environment (such as the aspirated word initial p of pot and the unaspirated final p of top) or if they do, the substitution of one for the other does not produce a different word, but merely a different pronunciation of the same word.
A phonetic alphabet must be enlarged to lend itself to convenient pattern recognition by an acoustic processor. The region of the recognition space used that corresponds to the acoustic variants of a given phoneme will have to be partitioned into a minimal number of compact, convex subregions, each subregion to be labelled by a different symbol, referred to as sub-phone The union of all of these subregions forms a convex region which is represented by a symbol called a phone. Since the design of the structure of the speaker production model and of the acoustic processor should be guided by phonetic experience, it will be desirable to keep the partitioning system such that the obtained subregions are made up of more or less traditional perception units. For a more detailed discussion see Continuous Speech Recognition by Statistical Methods Frederick Jelinek, Proc. of the IEEE, Vol 64, No. 4, pp 532-556 (April 1976), herein incorporated by reference.
Traditional speech recognition systems have used Hidden Markov Models (HMM's) to represent a phoneme or to represent a label. See U.S. Pat. No. 4,819,271 to Bahl et al., herein incorporated by reference. Context-dependent speech modeling systems typically utilize Hidden Markov Models. Hidden Markov Models are well known in the field of speech recognition. In general a Hidden Markov Model is a sequence of probability distributions, states and arcs. Arcs are also called transitions. An observation vector is an output of the Hidden Markov Model. Associated with every arc is a probability distribution, e.g., a gaussian density. The probability distribution are distributions on a series of observation vectors that are produced by an acoustic processor. When performing Hidden Markov Model speech recognition, one essential step is characterizing each word in a vocabulary as a sequence of Hidden Markov Models. Depending upon the model, each Hidden Markov Model represents either an entire word or a phoneme.
A Markov Model speech recognition system typically includes an acoustic processor which converts a speech input into a string of labels. The labels in the string are assigned to the output of the acoustic vector by utilizing a set of predefined prototypes where each prototype corresponds to a cluster of vectors in n-dimensional space that defines all speech. Based upon the values of the n characteristics, an n-component acoustic parameter vector is defined. An acoustic parameter vector is one type of feature vector. As discussed supra speech is categorized into convex regions. Each convex region has a prototype vector associated with it. A prototype vector is a representative vector for the convex region. A selection is made as to which convex region a given acoustic parameter vector belongs in. In general, when determining which convex region an acoustic parameter vector should be associated with, the acoustic parameter vector is compared to the prototype vector associated with each label. As stated earlier, each convex region is identified with a respective label. For each interval of time, typically a centisecond, the acoustic processor generates a signal representing an acoustic parameter vector; the convex region into which the acoustic parameter vector belongs is then determined; and the label for that convex region is associated with the time interval. The acoustic processor thus produces a string of labels as its output. Context-independent speech recognition systems model a given phoneme individually. Context-dependent speech recognition systems model a given phoneme utilizing the preceding and/or following phoneme. In order to adequately model context-dependent speech, significantly more convex regions are necessary than when context-independent speech is modelled.
Context-independent label prototype vectors are determined using an individual phoneme. The preceding or following phoneme are not considered during the development of these context-independent label prototype vectors. However, when words are spoken, a particular phoneme actually varies depending upon the previous phoneme and/or the following phoneme. The articulation of a sound may vary substantially when articulated in context compared to the articulation of the sound in isolation. Thus, depending on what sounds precede and follow a phoneme, the pattern of energy concentrations of a phoneme will change. Therefore, creating label prototype vectors which account for neighboring phonemes improves modelling of continuous speech. Accounting for the phonetic context results in context-dependent label prototype vector signals. Each phoneme has many variations depending upon the neighboring phone. The combination of a target phoneme and neighboring phonemes is called the phonetic context of the target phoneme.
Another type of Hidden Markov model based speech recognition system relies on arc ranks as derived from context-dependent arc prototypes. In general, this type of system reduces an inputted speech signal into signals representing a sequence of continuous valued acoustic parameter vectors by an acoustic processor. Then each arc assigns a conditional probability to each acoustic parameter vector. A rank processor then sorts these conditional probabilities and outputs the rank of each acoustic parameter vector based upon these conditional probabilities. Such a system is termed to be based upon arc ranks.
In a Hidden Markov Model arc rank speech recognition system training and recognition are performed in terms of ranks and not in terms of acoustic labels. In order to model speech with greater accuracy than other systems, context-dependent arc prototypes are used. Using arc ranks obviates the need of a labeller when training or recognizing a speaker. However, developing a recognizer requires the use of acoustic labels for some purposes such as the automatic creation of Hidden Markov Model word models. Therefore a labeller that can operate on context-dependent prototypes is a necessity.
In order to label a frame of speech using context-dependent label prototype vector signals it is desirable to know the exact phonetic context of the frame. In practice this can never be known exactly, but it can be estimated from a Viterbi alignment. In general, the Viterbi alignment aligns each label with its corresponding phone. Since training data is used, the phonetic context of each phone is known. Therefore the Viterbi alignment results in the alignment of the labels with its phonetic context. The Viterbi alignment is further defined below. It is often impractical to Viterbi align large quantities of speech on the basis of ranks. A more efficient method of alignment uses acoustic labels. The problem is that acoustic labels are needed in order to compute a Viterbi alignment, and an alignment is needed in order to determine the labels, which are based upon context-dependent label prototype vector signals. What is needed is a method to resolve this mutual dependency.
The present invention is a system and method for creating labels for speech recognition using context-dependent label prototype vectors. The system and method comprise a means for accepting training data. This training data is then labelled. After labelling, the data is tagged, i.e., associated with the proper phonetic context. The context-dependent label prototype vector which maximizes the likelihood of obtaining each frame of the tagged training data is identified. The label associated with the context-dependent label prototype vector with the highest likelihood is also associated with that frame of tagged training data.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of the preferred embodiments of the invention, as illustrated in the accompanying drawings, wherein:
FIG. 1 is an illustration of a computer system incorporating the present speech recognition system;
FIG. 2 is a more detailed illustration of the modules comprising the present speech recognition system;
FIG. 3 is a simplified sketch of a method for extracting acoustic parameter vectors from a speech signal;
FIG. 4 is an illustration showing the modules of the context-dependent labeller of the present invention;
FIG. 5 is a flow diagram for the overall speech recognition system and method;
FIG. 6 is a two dimensional representation of a number of acoustic parameter vectors in space and the division of the feature vectors in a number of convex regions;
FIG. 7 is a diagram showing the correlation between a string of text and different sounds; and
FIG. 8 is a diagram showing how the initial steps of the present invention affect the training data.
The System and method of the present invention involves model construction for arc rank recognition systems. The invention creates the labels necessary for leafemic baseform construction when the label prototype vectors are context-dependent and the context of a frame of speech is not known with certainty.
There are several modeling units that can be used as the baseform for speech modeling. Speech can be modelled a word at a time, or as fenones, i.e., short units of speech. Another technique is to model a word as a sequence of leafemes. A leafeme is a context-dependent model which corresponds to each phoneme in a word. The present invention utilizes leafemes as its model baseform.
FIG. 1 illustrates an environment in which a preferred embodiment of the present invention operates. The preferred embodiment of the present invention operates on a computer platform 104. The computer platform 104 includes certain hardware units 112 including one or more central processing units (CPU) 116, a random access memory (RAM) 114, and an input/output (I/O) interface 118. The computer platform 104 includes an operating system 108, and may include microinstruction code 110. A speech recognition system 103 may be part of the micro instruction code 110 or an application program to be executed via the operating system. Various peripheral components may be connected to the computer platform 104, such as a terminal 126, a data storage device 130, and a printing device 134.
FIG. 2 is a more detailed illustration of a speech recognition system 103. A user interface 202 may include a microphone for generating an analog electrical signal corresponding to a speech utterance. An analog to digital (A/D) converter may also be included in the user interface 202. The user interface 202 is coupled to a signal processor/feature extractor 204. The signal processor/feature extractor 204 receives the electrical signal output from the user interface 202 and produces signals representing a set of feature vectors, hereinafter feature vector signals, e.g., acoustic parameter vectors.
FIG. 3 illustrates this process in greater detail. Signal processor/feature extractor 204 can include electronic components such as an analog-to-digital converter (A/D) and a spectral analyzer. The output of the user interface 202 is coupled to the signal processor/feature extractor 204. For each interval of time, i.e., a frame, the signal processor/feature extractor 204 examines n-characteristics of speech. Based upon the values of the n characteristics, a signal representing an n-component feature vector 306 is defined. In the preferred embodiment, the signal processor/feature extractor 204 extracts signals representing a series of acoustic parameter vectors from the speech signal. One type of acoustic parameter vector signal, hereinafter acoustic parameter vector, that can be extracted is based upon the frequency of the signal. Measurement and spectral analysis of the speech signal by the signal processor/feature extractor 204 creates a frequency sensitive acoustic parameter vector. The spectral analysis comprises measuring the energy of the speech signal for each time unit in a number of different frequency bands n, which may be approximately 20. The time unit, i.e., frame, is small. Therefore the creation of many acoustic parameter vectors occurs every second.
The output of the signal processor/feature extractor 204 is coupled to a context-dependent labeller 206. The context-dependent labeller is also coupled to a prototype storage mechanism 208. For each feature vector the context-dependent labeller 206 computes and associates a score for every context-dependent label. The context-dependent labeller 206 then associates each feature vector with the context-dependent label having the highest score. The scores for each context-dependent label and the tagged feature vectors are output from context-dependent labeller 206. The context-dependent labeller 206 is coupled to a rank determinator 210. The rank determinator ranks the scores which were calculated in the context-dependent labeller 206.
A matcher 212 is coupled to a rank determinator 210, a word model storage 214 and a hypothesis searcher 216. The matcher 212 receives a word model from the word model storage 214 and the determined ranks from the rank determinator 210 and computes a score for each word model. This score is output to the hypothesis searcher where the word model with the highest score is chosen. The chosen word represents the system's determination as to the identification of the speech utterance.
FIG. 4 illustrates the modules of the context-dependent labeller 206. The memory module 402 can be one or a combination of more than one data storage devices, e.g. floppy disks or random access memory (RAM). The preferred embodiment of the invention uses a computer's internal random access memory (RAM) as the memory module 402. A context-independent labeller 404 is coupled to the memory module 402 and an aligner 406. The aligner 406 is coupled to the memory module 402 and a tagger 408. The tagger 408 is coupled to the memory module 402 and a comparator 410. The comparator 410 is coupled to the memory module 402 and a probability determinator 412. The probability determinator is coupled to the memory module 402 and an assignor 414. The assignor is coupled to the memory module 402. The input data and output data for the context-dependent labeller 206 is stored in the memory module 402.
FIG. 5 illustrates the method of the context-dependent labeller's method of operation. Acoustic parameter vectors have been stored in the memory module by the speech processor/feature extractor 204. The context-independent labelling of these acoustic parameter vectors, step 504, occurs in the context-independent labeller 404. A label alphabet is known and has been stored in the memory module 402. Each label has a set of context-independent and context-dependent label prototype vector signals associated with it. These prototype vector signals have also been stored in the memory module 402 and are described below. A method for constructing context-dependent prototype vectors is discussed further in L. R. Bahl et al., "Context Dependent Vector Quantization for Continuous Speech Recognition", Proceedings from the International Conference on Acoustics, Speech, and Signal Processing, Minneapolis Minn. (April 1993), herein incorporated by reference. The construction of the context-independent prototype vectors are described below. The assignment of these labels to an acoustic parameter vector in step 504 uses context-independent label prototype vectors. Acoustic parameter vectors are labeled as different classes of sound in a process referred to as vector quantization, described below.
One technique for vector quantization involves separating different speech vectors into different clumps within memory module 402. Referring to FIG. 6 these clumps are divided into regions known as convex regions 602, 604. For simplicity in FIG. 6 we examine training data in the form of acoustic parameter vectors with only 2 dimensions. Such clumping of the different acoustic parameter vectors into regions is referred to as clustering. In essence the process of clustering involves calculating a mean value for each convex region by computing the respective mean values of the training vectors in each region. In FIG. 6 dots represent the training vectors 606, 608. The resulting mean value of the training vectors in each region is referred to as a prototype or a prototype vector 610, 612. Prototype vector 610 represents the mean of all training vectors in convex region 604. These prototype vectors are stored in the prototype storage 208. In the preferred embodiment the prototype storage is located within the memory module 402. An identifier is assigned to each prototype vector. Each prototype vector also may be referred to as a class of sound. Context-dependent speech recognition systems and context-independent speech recognition systems each require a different set of prototype vectors. Both types of prototypes are stored in the memory module 402. Since context-dependent speech modelling models combinations of sounds, more classes of sounds, each having a prototype vector, exist.
In the preferred embodiment the method for discovering what class of sound an acoustic parameter vector is associated with is accomplished by determining the distance separating the acoustic parameter vector and the label prototype vector. The label prototype vector having the minimum difference is associated with the acoustic parameter vector. The identifier of that prototype vector is used to label this acoustic parameter vector. "Vector quantization" is the name of this process.
Each acoustic parameter vector must be identified as corresponding to a speech sound, or phoneme. In speech recognition each word is ordinarily represented as a string of speech sounds in memory module 402. For example, the model "k ae t" represents the word "cat." Since English spelling is irregular, the mere fact that a person knows the pronunciation of the word and the spelling of the word does not necessarily allow him to figure out what speech sounds represent the word. In the preferred embodiment each word maps onto a string of speech sounds or phonemes. Thus, a string of phonemes makes up each word. A phone is the symbol representing the phonemes. It requires an inventory of approximately 80 phones to enable a vector quantizer to generate the labels associated with the different sounds that make up the English language. Of course for a different language an inventory having a different number of sounds and labels is necessary.
Correlating the different phones with the acoustic parameter vectors generated by the utterance of a word is done in the aligner module 406 using the well known "Viterbi Alignment" technique in step 508. For a more detailed discussion of the Viterbi Alignment technique, see F. Jelinek, Continuous Speech Recognition by Statistical Methods, Proc IEEE Vol. 64, 532-556, 1976, incorporated herein by reference. In essence, the Viterbi alignment technique uses Hidden Markov Model technology to align or correlate each phone in a given text for the purpose of determining what label (i.e. the output from the vector quantizer) each phone corresponds to. For illustration, FIG. 7 shows the text string "The Cat In The Hat" and the phones representing the sounds (phonemes) of this text string. The Viterbi Alignment aligns the phones and labels of the text string. That is, each phone has been identified with a corresponding label. For example the phone "dh" for the word "The" corresponds to labels L1 and L2. The phone "uhl" corresponds to the labels L3, L4 and L5. Each acoustic parameter vector is associated with a context-independent label in step 504. After the correspondence between the labels and each phone is known, it follows that the acoustic parameter vector corresponding to each phone in the text string is also known, as shown in FIG. 8. Accordingly, correspondence between acoustic parameter vectors and phonemes can be established by the Viterbi alignment technique.
The context-independent label prototype vectors are based upon each individual phoneme without regard to the preceding or following phoneme. However, when speaking words, a particular phoneme actually varies depending upon the previous phoneme and/or the following phoneme. The articulation of a sound may vary substantially when articulated in context compared to when the sound is articulated in isolation. Thus, depending on what sounds precede and follow a phoneme, the pattern of energy concentrations of a phoneme will change. For example the phoneme "s" varies depending on whether it is followed by the phoneme "oo" like "sue" or by the phoneme "e" like "see." Therefore creating label prototype vectors which account for neighboring phonemes better models continuous speech. Accounting for the phonetic context results in context-dependent label prototype vectors. Each phoneme has many variations depending upon the neighboring phonemes. The phonetic context of the target phoneme is the combination of a target phoneme and neighboring phonemes. The marking of the training data with its phonetic context has occurred before the training data enters the invention. Therefore the exact phonetic context of each phone in the training data is known.
One goal of the present invention is to determine the most likely label sequence for each word in the system's vocabulary. Thousands of prototype vectors are necessary to model context-dependent speech systems. The Viterbi alignment 508 aligns the acoustic parameter vector with the corresponding phone. Since training data is being analyzed the alignment between the phone and the phonetic context is also known. As shown in FIG. 8, after the data alignment, the phonetic context for each acoustic parameter vector is also known.
After the acoustic parameter vector signals have been processed by the aligner 406 they are operated upon by the tagger module 408. Therefore, the next step is that each acoustic parameter vector signal is tagged, or associated with, the appropriate phonetic context in step 510. The tagger stores the tagged feature vector signal, in the preferred embodiment a tagged acoustic parameter vector signal, in the memory module 402.
Steps 512-520 are performed for each tagged acoustic parameter vector. An acoustic parameter vector is selected in step 512. Determining which label should be associated with each acoustic parameter vector using context-dependent analysis is one accomplishment of the present invention. Initially the invention, in step 514, must identify the context-dependent label prototype vector corresponding to the phonetic context of a given acoustic parameter vector for each label. The comparator module 410 operates on the acoustic parameter vector after it has been tagged in the tagger module 408. For each label in the label alphabet the comparator module 410 determines whether each label has a prototype vector corresponding to the phonetic context associated with the tagged acoustic parameter vector. If such a prototype vector exists the probability determinator 412 determines the probability of obtaining the tagged acoustic parameter vector given this prototype vector in step 516. In the preferred embodiment the probability determinator 412 determines the probability using Hidden Markov Models. The comparator 410 and probability determinator 412 continue this process for each label of the label alphabet.
The comparator module 410 and probability determinator module 412 determine the probability of achieving the acoustic parameter vector for each label having a context-dependent prototype which corresponds to the same phonetic context as said tagged acoustic parameter vector, step 516, and stores this information in the memory module 402. The assignor module 414 obtains this information from the memory module 402 and identifies the context-dependent prototype vector having the highest probability of producing the acoustic parameter vector in step 518. The assignor 414 retrieves the label associated with this context-dependent prototype vector from the memory module 402 and replaces the old label associated with the acoustic parameter vector in step 520. Recall that the old label associated with the acoustic parameter vector is the result of context-independent labelling that occurs in the context-independent labeller 404 during step 504.
At this point the given acoustic parameter vector is identified with a label based upon a context-dependent prototype vector. This information is stored in the memory module 402. Steps 512-520 are repeated for all acoustic parameter vectors as shown in step 522.
After the identification of all acoustic parameter vectors is complete steps 508-522 may be repeated until convergence in the sequence of labels is obtained at step 524. When repeating steps 508-522 the labels and the phones may be aligned differently in step 508, if they are aligned differently then the labels assigned to the acoustic parameter vectors in step 520 may be different and therefore convergence is not achieved in this iteration. The identification as well as the score for each label/acoustic parameter vector combination is stored in the memory module 402. Subsequent Hidden Markov Modules are derived from the new label information which may be used, for example, in an arc rank system.
One measure of performance for speech recognition systems is the phone/label mutual information. The phone/label mutual information is a measure of how the phone and label correspond to each other. For example, if a phone uniquely identifies a label the phone/label mutual information is at a maximum for this phone and label. If two phones correspond to one label the phone/label mutual information is less for these phones and label, i.e., the correspondence is not as "sharp". The mutual information is reduced when more labels can be output for a given phone, or when more phones can correspond to a particular label. If every phone produced exactly the same distribution of labels the mutual information between phones and labels is zero because if a phone is given no information can be gleaned as to what label is produced.
One iteration of the method of the current invention was applied to five hundred sentences of continuous speech obtained from a single speaker. The phone/label mutual information obtained with phoneme-based Hidden Markov Model word models and a label alphabet size of 210, improved from 3.25 to 3.53 bits when replacing the labels from the context-independent labeller 404, i.e., replacing the labels from step 504 with the labels from the assignor module 414 in step 520. The labels of step 520 were obtained from 12,093 context-dependent label prototype vectors each of which consisted of a mixture of two diagonal gaussians.
While the invention has been particularly shown and described with reference to a preferred embodiment thereof, it will be understood by those skilled in the art that various change in form and details may be made therein without departing from the spirit and scope of the invention.
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Effective date: 20031003


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Familienpension Linder Seeboden is perfectly positioned in peaceful surrounds in Seeboden, just a short car ride from Burgruine Rothenthurn and Burgruine Hohenburg. Some of the premium features at this 3-star hotel include outdoor tennis courts and miniature golf.
Hotel description
Familienpension Linder has a golf course, a playground and an express check-in and check-out feature. Those staying at the property are welcome to exercise in the gym, or borrow a bicycle and discover the surroundings.
The comfortable rooms at Familienpension Linder Seeboden offer a radio, a mini bar and a desk. A hair dryer, a shower and cable/satellite channels are also provided.
A satisfying breakfast is served each morning at Familienpension Linder, and there are numerous restaurants and cafés close by. For those spending the day out, the hotel offers packed lunches on request.
For those wanting to explore the great outdoors, Familienpension Linder Seeboden is near popular fishing and scenic spots. The staff at the tour desk are available to book tours and sightseeing trips in Seeboden.
Hotel features
Pet Friendly, Non-Smoking Rooms, Mini Bar, Cable / Satellite TV, Hair Dryer, TV, Shower, Highchairs
Dry Cleaning, ATM / Cash Machine, Laundry service, Bicycle Rental, Photocopier, Desk, Luggage Storage, Express Check-In/Check-Out
Fitness Room/Gym, Tour Desk, Tennis Courts, Golf Course, Garden, Water Activities, Fishing, Playground
High-speed Internet, Internet Connection (wireless)
Golf (miniature), Horse Riding, Hiking, Packed Lunches
Availability and Prices
Booking Site
Room Type
Per room
Per night
Per night
Users who viewed Familienpension Linder Seeboden also viewed these 3 stars Seeboden hotels:
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Gmeineckweg 17, Seeboden
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Bootsweg 5, Seeboden
Bootsweg 5, Seeboden
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- Ferienapartment Seepark
0.1 miles
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Previous Challenge Entry (Level 1 – Beginner)
Topic: Huh? (01/21/10)
TITLE: One lone tomato
By Tessy Fuller
Earlier I had sat outside the dressing room waiting for her to come out and model a pair of jeans she was trying on. I could just envision her coming out and flashing me a smile letting me know I was the best mom in the entire world. However, my daydream was cut short when the door to the dressing room flew open and she stormed out of the store. When I finally caught up with her I asked �What happened with the jeans? I thought they were the ones you have been wanting.� She had replied �Mom, can we not talk about it?� and then flicked her hair with such force I thought it was very possible she may have given herself whiplash. �I just don�t understand why you are so upset.� I had inquired still searching for any inkling I could get to understand this person who now lived inside my daughter. �You really want to know?� she challenged. �I didn�t get them because they didn�t fit because I am fat. Now you know, are you happy?�
It is so strange for me to be in this place where a mother and daughter can exist as strangers. Not long ago she was climbing into my lap, wrapping her little arms around my neck and telling me I was her best-friend in the whole world. When did my importance get rated right up there with chopped liver? Her salad is now abandoned and she is busy texting. I wonder if I should try to engage her in conversation again. It is a risky decision because my voice alone may make her want to jump off a cliff just so she can get away from the table. My mouth seems dry. I take a sip of soda.
�So Liv, are you about ready to go? I ask. � I�d like to go to Bath and Body works before we leave. Maybe you can help me pick out a good scent?� I look over at her waiting for her response. Her head is down. She is still seemingly texting though she could be just purposefully ignoring me. �Liv?� I prompt again. This time she looks up. �Huh?� she questions looking to see that I had gathered my stuff to go. �Oh, your ready to go. I�m glad because I am super bored. Can we go home now?� she asks as she slides her phone into her pocket. I ignore her and head to the trash can. I dump my tray and head back toward the mall. I can hear her footsteps behind me. �Are you seriously shopping some more?� she asks. I dig into my pocket and address her. �Here are the keys. You can go sit in the car until I am done. I just have one more store to go to and I would not want to inconvenience you by making you come along.� I quip back. I turn away quickly as tears spring to my eyes.
I spend the next ten minutes sitting on a bench trying to pull myself together. There is no way I�m going to let her know how much she got to me. In the midst of my pity party my phone vibrates. I grab it and flip it open. I have a text message. It reads �I�m sorry� and to my astonishment it is from Liz and probably to her surprise I find myself texting back. In a series of texts later I find out that one of her �friends� at school had let her borrow a pair of jeans and when she tried them on they didn�t fit. Her friend then made some remarks about her needing to lose some weight acting like she was joking. However, Liz had taken it to heart.
After ending our text conversation I headed back to the car. Not a single word was said on the way home. I wasn�t sure about this new way of communicating but it did get us past the �Huh?� level and for that I was thankful.
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Life in Nordic countries carries on as usual. We expect this shit to happen every Winter. Woke up to crisp -25C this morning. House temperature still at normal comfy +21C. Jumped in to my car and drove to office like I do every day. Did not even scratch the car windows as I have a timer operated heater in it (pretty much standard here). It's all about how well you are prepared for weather like this.
2/9/12 at 11:23pm


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PrivateSexTapes - Logan - Sexy Blond Ass Fucking [HD 720p]
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When the NCAA started requiring 820 SAT or 68 ACT scores for freshmen to be eligible for college sports, longtime college basketball coach Jerry Tarkanian predicted that there would be cheating on those standardized tests to make star athletes eligible.
He's been proven correct in several cases. The most notable one involved current NBA star Derrick Rose, who led Memphis University to the NCAA men's Division I championship game in his only season there.
LEOPOLD – The Perry Central Music Boosters are asking for donations from the community for the annual silent auction to be held April 16.
For more information about where to drop off items, contact Cathy Blair at 843-4174.
TELL CITY – Local Republicans will host their annual Lincoln Day Dinner Saturday, April 16, at Bill & Dave's Smokin' Pit in Tell City. The meal will begin at noon.
Rep. Todd Young will be the guest speaker.
Tickets are $12 and can be purchased by calling Tom McCart at 547-6156 or Jennifer Land at 547-3158.
PERRY COUNTY – During a period when commodity markets are showing high and volatile prices, farmers face the risk of wide fluctuations in farm income. On Tuesday, a workshop will be held at the Southern Indiana Purdue Agriculture Center in Dubois from 9 a.m. to 1 p.m.
PERRY COUNTY – The Perry County Power of Youth, a program co-sponsored by the Perry County Community Foundation and United Way of Perry County in partnership with Purdue Extension-Perry County, is accepting applications for its 2011-12 council.
TELL CITY – On March 8 the Pocket Athletic Conference Academic Super Bowl competition was held at the Bryan Taylor Sports Arena. Eight of the nine PAC teams participated.
For the fourth year in a row, Tell City dominated the competition. English, science, social studies and math teams captured first-place honors. The fine-arts and interdisciplinary teams earned second-place finishes. Tell City captured the championship with a total score of 100 points.
TELL CITY – The Perry County Chamber of Commerce will host a legislative breakfast at 8:30 a.m. Saturday at Bill & Dave's Smokin' Pit.
State Sen. Richard Young and State Rep. Sue Ellspermann will be available to speak about the current session and to answer questions and hear concerns from those attending. Other legislators have been invited, The public is invited to attend this event. Breakfast will be available for purchase.
TELL CITY – Golden Living Center-Lincoln Hills will host a 106th birthday celebration in honor of Julia Storm from 2 to 3 p.m. Thursday.
The celebration will be held in the Station 1 dining room.The public is invited to attend and wish Julia a happy birthday.
Anyone wanting to send her a birthday greeting can write to her at Golden Living Center-Lincoln Hills, 402 19th St., Tell City, IN 47586.
By VINCE LUECKE, Editor
TELL CITY – A Tell City man who allegedly fled from a March 1 traffic stop has been charged in Perry Circuit Court with several felony drug crimes, including a Class B felony count of dealing in methamphetamine.
Randy L. Cole, 23, 1012 13th St., also faces charges of possessing meth and ingredients used in its making and a separate felony offense of possessing an altered handgun.
By KEVIN KOELLING, Managing Editor
PERRY COUNTY – Benches being produced through a partnership of several organizations are being offered for sale as a fundraising venture.
Proceeds will go toward a required match for a Welborn Baptist Foundation grant funding the Perry County Greenway, said steering-committee member Rebecca Fenn. Buyers can place the benches on their private property or any public property for which they obtain permission, she said.
Copyright © 2017
The Perry County News is your source for local news, sports, events, and information in Perry County, Indiana, and the surrounding area.


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When out and about, plan time with your little ones to stop and look in the windows and talk about what you see. Different things may catch their eye, and the sharing will be fun. Many stores have fantastic ornaments for sale. You can start a new tradition of buying a special one each year to add to your tree.
Christmas tree lots have displays to delight everyone. Whether you are in the market for a tree or not, it might be fun to stop by, and perhaps you will decide to purchase a wreath or mistletoe.
December's long, dark evenings are perfect for a drive around town to enjoy the decorated homes. Some displays are too intricate to be enjoyed from the car; again, allow time to get out and explore, especially at Bob's World, 2612 Calle Reynoso. LightsoftheValley.com will help you find others.
When you return home, sit in front of the fire to enjoy a cup of hot chocolate, talk about everything you've seen, and maybe brainstorm about the perfect holiday front yard. Or you may want to pull out paper and crayons for the newly inspired youngsters to create Christmas Art 2013.
Your children will long remember the fun of peeking in gaily decorated shop windows and enjoying other Christmas displays with you. It's all part of the magic.
--Dolores Fox Ciardelli
This story contains 292 words.
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SALT LAKE CITY -- While the University of Utah waits to see whether head coach Urban Meyer is lured away by the University of Florida, one of his top assistants has become a candidate to move up as well.
Utes offensive coordinator Mike Sanford will interview Monday to become the head coach at UNLV, where veteran John Robinson coached his final game Saturday. Sanford, 49, coached for Robinson at USC during Sanford's eight seasons with the Trojans (1989-96).
Robinson, who won a national championship at USC in 1978, announced his retirement earlier this season. The Rebels lost their final five games this season to finish 2-9, 1-6 and last in the Mountain West Conference.


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If it's a wide and varied range of projects you want to sink your engineering teeth into, you'll find them all at City Care. Here, our engineers come from all different disciplines and we make sure they're given the opportunity to further enhance their skills in our various divisions.
At City Care, we're proud of the work we do in our communities, and we're especially proud of our people who are totally committed to making it work for our customers. Right now we're looking for committed, motivated people to help rebuild Christchurch.
With a large and varied range of industry sectors under our belt, City Care is the cream of the crop for Quantity Surveyors. We cater to over two million New Zealanders across the country which means continuous, challenging, and diverse work.
You'll be pleased to know that there's not much (if any) of the trades industry we don't cover. Which is great news for you. No matter what area of the industry you come from or want to get clued up on, there's sure to be something at City Care that fits you just right.
As New Zealand's largest and most trusted provider of water, wastewater, and stormwater services, it's vital to us that we've got talented and highly skilled people in our Three Waters division. And as part of that team, you'll have the opportunity to play a role in the upkeep of over 19,000kms of piping networks across the country.
Providing quality infrastructure services to over two million New Zealanders in 17 locations across the country is no mean feat. Yes, we've got a lot of great people out and about keeping things ticking over, but there's also loads of talented people doing their bit from within four walls.
The great outdoors – something New Zealand does pretty well, isn't it? If you've got a green thumb or just like being out and about on a daily basis, you'll find everything you need in a job with City Care. You see, we're the biggest and most experienced provider of open space maintenance in the country.
If big machines are your thing, you'll find plenty of them at City Care. We've always got a number of construction projects going on across many different divisions throughout the country, so no matter what type of machine you operate, there's bound to be something for you.
No matter where you are in your career – new to the workforce looking to develop your skills or winding down and keen on a job that allows you to clock in and clock out – City Care has something for you. We span a huge range of industry sectors throughout New Zealand and we're always in need of labourers.
Whether you're an experienced STMS or a rookie looking to launch your career, you'll find your dream role in our Traffic Management team.
Congrats to our SCIRT delivery team who took our the March 2016 SCIRT Bill Perry Award for their Good to Go - Working Safely Around Services video and supporting reference guide.
The programme to update our diesel fleet's catalytic converters has begun! We're aiming to have all vehicles running on the new converters. Watch this space...
Christchurch rebuild team members Josh Tomlin and Pete Barrett work into the evening tiling the roof of a new unit built to re-home local residents. It's just one of many in the subdivision. It's just one of the many subdivisions that City Care has been involved with.
Our incredible clean-up crew managed to clear the wreckage after the Wellington floods in record time. Nice work, guys!
The joint venture between City Care and JFC Limited is delivering key anchor projects in the Christchurch rebuild.
Check out more City Care stories here


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Iberostar Hacienda Dominicus: Find photos, reviews, information, amenities and compare prices for 5 stars hotels in Bayahibe, Dominican Republic.
Situated in Bayahibe, Iberostar Hacienda Dominicus is within a 25-minute drive of La Romana International Airport and provides a private beach, an outdoor pool and a Jacuzzi. A swimming pool and a sauna are just some of the deluxe features available to guests throughout their stay.
Hotel description
There are a range of amenities on offer to those staying at Hacienda Iberostar Dominicus, such as a kids pool, an all-inclusive meal plan and massage services. Staff are available 24-hours a day and can assist with booking tours and tickets. Babysitting/child services, a currency exchange and meeting rooms are also on offer at the hotel.
Every morning, guests can wake up to breakfast served by Hotel Iberostar Hacienda Dominicus before venturing out to discover what the surrounding area has to offer.
Hotel features
Disabled Access, Air Conditioned, Disabled Accessible Rooms
24 Hour Reception, Babysitting / Child Services, Conference Room(s), Currency Exchange, Multilingual Staff, Meeting Rooms, Shops, Shops in Hotel
Fitness Room/Gym, Swimming pool, Tour Desk, Massage / Beauty Centre, Sauna, Garden, Jacuzzi, Massage, Spa & Wellness Centre, Pool Outdoor
Private beach, Nightclubs, Child Pool, Souvenirs/Gift Shop
Availability and Prices
Booking Site
Room Type
Per room
Per night
Per night
Users who viewed Iberostar Hacienda Dominicus also viewed these 5 stars Bayahibe hotels:
Dreams La Romana
Playa , Bayahibe
Playa , Bayahibe
Appartamento Ibiza 25
Boulevard Dominicus Americanus, Bayahibe
Boulevard Dominicus Americanus, Bayahibe
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Hip hop duo Untouchable to return from military service together
TS Entertainments Hip hop duo Untouchable (Sleepy and The Action ) will be returning from their military service together on May 1st.
The pair enlisted together back in August 2011 at the 306th reserves, and now their service is finally coming to an end.
After the end of their service on May 1st, the duo will be taking a short break before making a comeback to the music scenes as Untouchable once again. The duo had gained much fame with their single You You in May 2011 right before they enlisted, so hopefully theyll come back with great music for their fans again!
Image: Newsen
Tip: junwooram


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Written on: 16/03/2006 by pibul302 (1 review written)
Good Points
Bad Points
Crash,crash and crash.
General Comments
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Get an overview of this hotelHotel in Grand Junction with free breakfast and seasonal outdoor pool
Situated in Grand Junction, this hotel is within 3 mi (5 km) of Colorado Mesa University, Western Colorado Center for the Arts, and Grand Junction Golf Course. Museum of the West and Two Rivers Convention Center are also within 6 mi (10 km).
Hotel Features
Along with a seasonal outdoor pool, this hotel has a children's pool and laundry facilities. Free continental breakfast, free WiFi in public areas, and free self parking are also provided. Additionally, a 24-hour front desk and RV/bus/truck parking are onsite.
Room Amenities
All 128 rooms offer free WiFi, coffee makers, and TVs with cable channels. Free local calls and hair dryers are also available to guests.Information missing or incorrect? Tell us!Opens in a new window
Super 8 Grand Junction Colorado
Hotel Amenities
Hotel Amenities
Super 8 Grand Junction Colorado features a seasonal outdoor pool and a children's pool. High-speed wireless Internet access is complimentary. A complimentary breakfast is offered each morning. This Grand Junction hotel also offers laundry facilities, RV/bus/truck parking, and an elevator (lift). Onsite self parking is complimentary. The property may provide extended parking privileges to guests after check-out (surcharge).
- 24-hour front desk
- Free WiFi
- Outdoor seasonal pool
- Number of buildings/towers - 1
- Total number of rooms - 128
- Number of floors - 3
- Free breakfast
- Elevator/lift
- Laundry facilities
- RV, bus, truck parking
- Free self parking
- Children's pool
Family Friendly Amenities
- Family Hotel
- Children's pool
- Free Wi-Fi
- Laundry facilities
Available in all rooms: Free WiFi
Available in some public areas: Free WiFi
Free self parking, RV, bus, truck parking
Room Amenities
- Private bathroom
- Cribs/infant beds (surcharge)
- Coffee/tea maker
- Premium TV channels
- Air conditioning
- Free local calls
- Hair dryer
- Television
- Cable TV service
- Free WiFi
- Rollaway/extra beds (surcharge)
Where to Eat
A complimentary continental breakfast is served each morning between 6 AM and 9 AM.
Nearby Things to Do
A children's pool and a seasonal outdoor pool are on site.
If you have requests for specific accessibility needs, please note them at check-out when you book your room.
- Accessible bathroom
- In-room accessibility
Hotel Policies
Check-in time starts at 3 PM
Check-out time is 11 AM
Payment types
Children and extra beds
- Children are welcome.
- Kids stay free! Up to 3 children 13 years old and younger stay free when using existing bedding.
- Rollaway/extra beds are available for USD 5 per night.
- Cribs (infant beds) are available for USD 5 per night.
- Pets allowed for an extra charge of USD 10 per pet, per night, plus a one-time cleaning fee of USD 25
- Up to 100 lb
You need to know
Extra-person charges may apply and vary depending on property policy.
Government-issued photo identification and a credit card or cash deposit are required at check-in for incidental charges.
Special requests are subject to availability upon check-in and may incur additional charges. Special requests cannot be guaranteed.
Optional extras
- Pet fee: USD 10 per pet, per night plus USD 25 one-time cleaning fee
- Crib (infant bed) fee: USD 5 per night
- Rollaway bed fee: USD 5 per night
Hotel Name
- Super 8 Grand Junction Colorado
- Super 8 Hotel Grand Junction Colorado
- Super 8 Grand Junction Colorado Hotel
- Super 8 Hotel Grand Junction
- Super Eight Grand Junction
- Grand Junction Super Eight
- Super 8 Grand Junction
- Grand Junction Super 8
We should mention
Up to 3 children 13 years old and younger stay free when occupying the parent or guardian's room, using existing bedding.


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|NORTH COUNTRY MUDDIGGER BULLDOGGES KIM & TODD GRAY DEAF PUPPY DERBY, Vermont Gave purchaser several options Author: RUTLAND, Vermont Animal Abuse: NORTH COUNTRY MUDDIGGER BULLDOGGES DERBY, Vermont
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