Abstract:
A sub-phoneme model given acoustic data which corresponds to a phoneme. The acoustic data is generated by sampling an analog speech signal producing a sampled speech signal. The sampled speech signal is windowed and transformed into the frequency domain producing Mel frequency cepstral coefficients of the phoneme. The sub-phoneme model is used in a speech recognition system. The acoustic data of the phoneme is divided into either two or three sub-phonemes. A parameterized model of the sub-phonemes is built, where the model includes Gaussian parameters based on Gaussian mixtures and a length dependency according to a Poisson distribution. A probability score is calculated while adjusting the length dependency of the Poisson distribution. The probability score is a likelihood that the parameterized model represents the phoneme. The phoneme is subsequently recognized using the parameterized model.

Description:
BACKGROUND 
       [0001]    1. Technical Field 
         [0002]    The present invention relates to speech recognition and, more particularly to a method for building a phoneme model for speech recognition. 
         [0003]    2. Description of Related Art 
         [0004]    A conventional art speech recognition engine typically incorporated into a digital signal processor (DSP), inputs a digitized speech signal, and processes the speech signal by comparing its output to a vocabulary found in a dictionary. Reference is now made to a conventional art speech processing system  10  illustrated in  FIG. 1 . In block  101 , the input analog speech signal from microphone  416  is sampled, digitized and cut into frames of equal time windows or time duration, e.g. 25 millisecond window with 10 millisecond overlap. The frames of the digital speech signal are typically filtered, e.g. with a Hamming filter  103 , and then input into a circuit  105  including a processor which performs a Fast Fourier transform (FFT) using one of the known FFT algorithms. After performing the FFT, the frequency domain data is generally filtered, e.g. Mel filtering to correspond to the way human speech is perceived. In conventional art speech processing systems, the choice of FFT algorithm produces a spectrum with Mel-frequency cepstral coefficients (MFCCs)  107 . 
         [0005]    Mel-frequency cepstral coefficients are commonly derived by taking the Fourier transform of a windowed excerpt of a signal to produce a spectrum. The powers of the spectrum are then mapped onto the mel scale, using overlapping windows. Differences in the shape or spacing of the windows used to map the scale can be used. The logs of the powers at each of the mel frequencies are taken, followed by the discrete cosine transform of the mel log powers. The Mel-frequency cepstral coefficients (MFCCs) are the amplitudes of the resulting spectrum. 
         [0006]    The mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. The mel scale, is a perceptual scale of pitches judged by listeners to be equal in distance from one another. The difference between the cepstrum and the mel-frequency cepstrum MFC is that in the MFC, the frequency bands are equally spaced on the mel scale, which approximates the human auditory system&#39;s response more closely than the linearly-spaced frequency bands used in the normal cepstrum. 
         [0007]    The Mel-frequency cepstral coefficients (MFCCs) are used to generate voice prints of words or phonemes conventionally based on Hidden Markov Models (HMMs). A hidden Markov model (HMM) is a statistical model where the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters, from the observable parameters. Based on this assumption, the extracted model parameters can then be used to perform speech recognition. The model gives a probability of an observed sequence of acoustic data given a word phoneme or word sequence and enables working out the most likely word sequence. 
         [0008]    In probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate and independently of the time since the last event. The probability P that there are l occurrences in an interval λ is given by Eq.1. 
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         [0009]    e is the base of the natural logarithm (e=2.71828) 
         [0010]    l is the number of occurrences of an event—the probability of which is given by the distribution function. l! is the factorial of l 
         [0011]    λ is a positive real number, equal to the expected number of occurrences that occur during the given interval. For instance, if the events occur on average  4  times per minute, and the number of events occurring in a 10 minute interval are of interest, the Poisson distribution is used with k=10×4=40. 
         [0012]    A Gaussian mixture model Γ consists of a weighted sum of M Gaussian densities: 
         [0013]    w i g i (x 0 ) used to measure probability p for a feature vector, say x 0 . Where 
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         [0014]    The Gaussian mixture model Γ is defined by weights w i , Gaussian functions g i  (x 0 ) and summation Σ i  for i=1 to M and denoted as such in Eq.3 
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         [0015]    With the log-likelihood (i.e. a score) of a sequence of T vectors, X={x 1 , . . . ,x T } given by Eq.4 which is a score equation. 
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         [0016]    During the training of the Gaussian mixture module Γ, an update of the Gaussian mixture model shown by equation Eq.3 for example is denoted by Eq.5. 
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         [0017]    The additional notation (‘̂’) in Eq.5 represents the updated states of the initial Gaussian mixture model Γ after a training step or steps. 
         [0018]    TIMIT is a corpus of phonemically and lexically transcribed speech of American English speakers of different sexes and dialects. Each transcribed element has been delineated in time. TIMIT was designed to further acoustic-phonetic knowledge and automatic speech recognition systems. It was commissioned by DARPA and worked on by many sites, including Texas Instruments (TI) and Massachusetts Institute of Technology (MIT), hence the corpus&#39; name. The 61 phoneme classes presented in TIMIT can been further collapsed or folded into 39 classes using a standard folding technique by one skilled in the art. 
         [0019]    Reference is now made to  FIG. 6  which illustrates schematically a simplified computer system  60  according to conventional art. Computer system  60  includes a processor  601 , a storage mechanism including a memory bus  607  to store information in memory  609  and a network interface  605  operatively connected to processor  601  with a peripheral bus  603 . Computer system  60  further includes a data input mechanism  611 , e.g. disk drive for a computer readable medium  613 , e.g. optical disk. Data input mechanism  611  is operatively connected to processor  601  with peripheral bus  603 . Operatively connected to peripheral bus  603  is sound card  614 . The input of sound card  614  operatively connected to the output of microphone  416 . 
         [0020]    In human language, the term “phoneme” as used herein is a part of speech that distinguishes meaning or a basic unit of sound that distinguishes one word from another in one or more languages. An example of a phoneme would be the ‘t’ found in words like “tip”, “stand”, “writer”, and “cat”. The term “sub-phoneme” as used herein is a portion of a phoneme found by dividing the phoneme into two or three parts. 
         [0021]    The term “frame” as used herein refers to portions of a speech signal of substantially equal durations or time windows. 
         [0022]    The terms “model” and “phoneme model” are used herein interchangeably and used herein to refer to a mathematical representation of the essential aspects of acoustic data of a phoneme. 
         [0023]    The term “length” as used herein refers to a time duration of a “phoneme” or “sub-phoneme”. 
         [0024]    The term “iteration” or “iterating” as used herein refers to the action or a process of iterating or repeating, for example; a procedure in which repetition of a sequence of operations yields results successively closer to a desired result or to the repetition of a sequence of computer instructions a specified number of times or until a condition is met. 
         [0025]    A phonemic transcription as used herein is the phoneme or sub-phoneme surrounded by single quotation marks, for example ‘aa’. 
       BRIEF SUMMARY 
       [0026]    According to an aspect of the present invention there is provided a method for preparing a sub-phoneme model given acoustic data which corresponds to a phoneme. The acoustic data is generated by sampling an analog speech signal producing a sampled speech signal. The sampled speech signal is windowed and transformed into the frequency domain producing Mel frequency cepstral coefficients of the phoneme. The sub-phoneme model is used in a speech recognition system. The acoustic data of the phoneme is divided into either two or three sub-phonemes. A parameterized model of the sub-phonemes is built, in which the model includes multiple Gaussian parameters based on Gaussian mixtures and a length dependency according to a Poisson distribution. A probability score is calculated while adjusting the length dependency of the Poisson distribution. The probability score is a likelihood that the parameterized model represents the phoneme. The phoneme is typically subsequently recognized using the parameterized model. Each of the two or three sub-phonemes is defined by a Gaussian mixture model probability density function P i , with Poisson length dependency P(l; λ): 
         [0000]    
       
         
           
             
               
                 
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         [0027]    The sampled speech signal is framed to produce multiple frames of the sampled speech signal. The summation Σ is over the number f of frames of the sub-phoneme. The characteristic length λ is the average of the sub-phoneme length l in frames from the acoustic data. The dividing of the acoustic data and the calculating of the probability score equation are iterated until the probability score approaches a maximum. With the probability score at a maximum the Gaussian parameters of the parameterized model are updated. The parameterized model is stored when the characteristic length converges. 
         [0028]    According to the present invention there is provided a method of preparing a sub-phoneme model given acoustic data corresponding to a phoneme, for use in a speech recognition system. The acoustic data of the phoneme is divided into either two or three sub-phonemes. A parameterized model of the sub-phonemes is built. The model includes Gaussian parameters based on Gaussian mixtures and a length dependency according to a Poisson distribution. 
         [0029]    According to another aspect of the present invention there is provided a computer readable medium encoded with processing instructions for causing a processor to execute the method. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0030]    The invention is herein described, by way of example only, with reference to the accompanying drawings, wherein: 
           [0031]      FIG. 1  shows a conventional art speech processing system. 
           [0032]      FIG. 2   a  shows a system for obtaining a phoneme model via a training method and recognition of a phoneme subsequent to the training, according to an embodiment of the present invention. 
           [0033]      FIG. 2   b  shows a system for recognizing phonemes using the sub-phonemes stored of  FIG. 2   a.    
           [0034]      FIG. 3   a  shows a typical graph of amplitude (arbitrary units) versus time (arbitrary units) for speech showing phoneme ‘aa’ according to an embodiment of the present invention. 
           [0035]      FIG. 3   b  shows further details of the phoneme ‘aa’ divided into 3 sub-phonemes according to an embodiment of the present invention. 
           [0036]      FIG. 4  shows a method for optimizing a phoneme model according to an embodiment of the present invention. 
           [0037]      FIG. 5  shows how a maximizing probability path of a phoneme divided into three equal sub-phonemes for speech recognition according to an exemplary embodiment of the present invention. 
           [0038]      FIG. 6  illustrates schematically a simplified computer system according to conventional art. 
       
    
    
       [0039]    The foregoing and/or other aspects will become apparent from the following detailed description when considered in conjunction with the accompanying drawing figures. 
       DETAILED DESCRIPTION 
       [0040]    Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below to explain the present invention by referring to the figures. 
         [0041]    Before explaining embodiments of the invention in detail, it is to be understood that the invention is not limited in its application to the details of design and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting. 
         [0042]    By way of introduction, an embodiment of the present invention is directed toward optimally dividing a phoneme into either 2 or 3 sub-phonemes not dependent on a word or sentence model. Consequently as a result of dividing a phoneme into either 2 or 3 divisions, a set of 130 to 150 sub-phonemes are produced independent of a particular language and may be used for subsequent speech recognition. 
         [0043]    Reference is now made  FIG. 2   a  which shows a system  20  for obtaining a phoneme model via a training method  204 , according to an embodiment of the present invention. Mel-frequency cepstral coefficients (MFCC)  107  ( FIG. 1 ) are input to a mixture module  204 . Mixture module unit  204  outputs to data base  206 . The phoneme model obtained via training method  204  and mixture model unit  204  is preferably a Gaussian mixture model. Mel-frequency cepstral coefficients (MFCC)  107  ( FIG. 1 ) have preferably been derived using a Hamming-Cosine window with a 16-8 KHz transform with anti-aliasing. 
         [0044]    Reference is now made to  FIG. 2   b  which shows a system  21  for recognizing phonemes using the sub-phonemes stored in data base  206  of  FIG. 2   a.  Mel-frequency cepstral coefficients (MFCC)  107  ( FIG. 1 ) are input to a recognition unit  208 . Recognition unit  208  receives an additional input from the output of data base  206 . Recognition unit  208  has two outputs; and the recognized phonemes and/or sub-phonemes  212  and their length in frames  210 . 
         [0045]    Recognition of a phoneme represented by the input of mel-frequency cepstral coefficients (MFCC)  107  ( FIG. 1 ) is performed by by recognition unit  208  by comparing the phoneme with phoneme/sub-phoneme models stored in data base  206 . 
         [0046]      FIG. 3   a  shows a typical graph  10  of amplitude (arbitrary units) versus time (arbitrary units) for a speech signal which shows a phoneme ‘aa’.  FIG. 3   b  shows phoneme ‘aa’ divided into three sub-phonemes; ‘aa 1 ’, ‘aa 2 ’ and ‘aa 3 ’ according to an embodiment of the present invention. In  FIG. 3   b,  each sub-phoneme has a block of frames f with each frame having approximately equal length d. 
         [0047]    Reference is now made to  FIG. 4  illustrating training method  204  for obtaining the phoneme model according to an embodiment of the present invention. In an exemplary embodiment of the present invention, phonemes are in accordance with the 61 phoneme classes of TIMIT folded into 39 categories of classification and phonemes are divided into either 2 or 3 divisions. 
         [0048]    Phonemes of the folded TIMIT database are input to conventional system  10  which outputs mel-frequency cepstral coefficients (MFCC) coefficients corresponding to the phonemes input from the TIMIT speech corpus. 
         [0049]    The phonemes are modeled with two or three sub-phonemes. Probability density function P z  is used for the state probability density functions for each phoneme including Gaussian mixture model probability density functions, P i   1 , and P i   2  (for 2 sub-phonemes) with Poisson length dependency (P(l 1 ; λ 1 ), P(l 2 ; λ 2 )) of 2 sub-phonemes shown in equation Eq.7. Probability density function P z  is used for the state probability density functions for each phoneme including Gaussian mixture model probability density functions, P i   1 , P i   2  and P i   3  (for 3 sub-phonemes) with Poisson length dependency (P(l 1 ; λ 1 ), P(l 2 ; λ 2 ), P(l 3 ; λ 3 )) of 3 sub-phonemes shown in equation Eq.8. Probability density function P z  is determined for all frames f of each sub-phoneme (either 2 or 3 sub-phonemes) in equations Eq.7 and Eq.8. 
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         [0051]    Sub-phoneme probabilities P i   1 , P i   2  and P i   3  correspond to the Gaussian mixture model of equation Eq.3, such that each sub-phoneme had its own Gaussian mixture model i.e. for P i   1  for example in Eq.9 
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         [0052]    A score equation is obtained by taking logs of both sides of equations Eq.7 and Eq.8, giving equation Eq.10 for a 2 sub-phoneme division of a phoneme and equation Eq.11 for a 3 sub-phoneme division of a phoneme. Probability score equations Eq.10 and Eq.11 and the phoneme model are embedded with the acquired acoustic data (for example amplitude, time/frequency, frames, blocks of frames, Mel-frequency cepstral coefficients  107 ) characterizing each sub-phoneme (‘aa 1 ’, ‘aa 2 ’ and ‘aa 3 ’) obtained using system  20 . 
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         [0053]    In probability score equations Eq.10 and Eq.11, probabilities P i   1 , P i   2  and P i   3  are found for a mixture model for sub-phonemes; ‘aa 1 ’, ‘aa 2 ’ and ‘aa 3 ’ respectively. Probabilities P i   1 , P i   2  and P i   3  are summed over all frames for each block of frames corresponding to sub-phonemes ‘aa 1 ’, ‘aa 2 ’ and ‘aa 3 ’. Probabilities P i   1 , P i   2  and P i   3  are derived in a first iteration of the division (step  400 ) of phoneme ‘aa’ into 3 sub-phonemes of for instance approximately equal length. Probabilities P i   1 , P i   2  and P i   3  in subsequent iterations are used to for subsequent divisions (step  400 ) of the phoneme model into 3 sub-phonemes. 
         [0054]    P 1  (l 1 ; λ 1 ), P 2  (l 2 ; λ 2 ) and P 3  (l 3 ; λ 3 ) in Eq.10 and Eq.11 represent the Poisson probability distribution functions for ‘aa 1 ’, ‘aa 2 ’ and ‘aa 3 ’ respectively with lengths l 1 , l 2  and l 3  being equal to the number of frames in each block and with characteristic lengths λ 1 , λ 2  and λ 3  being the sum of the lengths d of each frame divided by the number of frames in each block. 
         [0055]    Once the division of phoneme ‘aa’ into 3 sub-phonemes and a build of the phoneme model (step  400 ) is performed, the probability score value is calculated using probability score equation Eq.11 (step  402 ) for all sub-phonemes and frames using lengths l 1 , l 2  and l 3  determined in step  400 . The value of the probability score equation Eq.11 is checked (decision box  404 ) to see if the value of the probability score equation Eq.11, for new values of lengths l 1 , l 2  and l 3 , is maximized when compared to previous score calculations (step  402 ). If the probability score value of Eq.11 is not maximized (decision box  404 ) then characteristic lengths λ 1 , λ 2  and λ 3  are updated (step  406 ) according to the length (l 1 , l 2  or l 3 ) that maximizes the score equation (Eq.7) and the division (step  400 ) is repeated over all frames for each block of frames corresponding to sub-phonemes ‘aa 1 ’, ‘aa 2 ’ and ‘aa 3 ’. 
         [0056]    Once the score calculation is maximized, the phoneme model is further refined by updating (step  408 ) the Gaussian mixture models in equations Eq.7 and Eq.8 i.e. updating; P i   1 , P i   2  and P i   3 . Using equation Eq.8 for example P i   1 , P i   2  and P i   3  are updated by summing for all frames using the characteristic lengths l 1 , l 2  and l 3  of Poisson distributions P 1 (l 1 ; λ 1 ), P 2 (l 2 ; λ 2 ) and P 3 (l 3 ; λ 3 ). 
         [0057]    The updated phoneme model (step  408 ) is compared (decision box  410 ) to the phoneme model created originally in step  400 . If there is no convergence between the values of characteristic lengths λ 1 , λ 2  and λ 3  used for the phoneme model in step  400  and the values of characteristic lengths λ 1 , λ 2  and λ 3  used to update the phoneme model in step  408 , then step  402  is repeated. 
         [0058]    Subsequent comparisons in step  410  are between the update in step  408  and the storage done in step  406 . Once there is a convergence of characteristic length (λ 1 , λ 2  and λ 3 ) values between the present phoneme model (built in step  408 ) and the previous phoneme model (built in step  400 ), the training step for the phoneme model is complete and the phoneme model is stored in data base  206  (step  412 ). 
         [0059]    Reference is now made to  FIG. 5  which illustrates graphically a maximum probability path  500  of recognizing a phoneme ‘aa’ which has been stored in data base  206  as divided into three sub-phonemes (‘aa 1 ’, ‘aa 2 ’ and ‘aa 3 ’). In the example of  FIG. 5 , twelve frames are shown which are initially divided into four frames per sub-phoneme. Typically, phonemes to be recognized are input into recognition unit  208  according to their Mel frequency Cepstrum coefficients. Probabilities are illustrated graphically which correspond (in time) to 12 frames of phoneme ‘aa’. 
         [0060]    According to a feature of the present invention, an initial step in recognizing a phoneme, e.g. ‘aa’ involves an appropriate selection of the beginning of frame  1  and the end of frame  12  which intends to accurately approximate the overall length of the phoneme to be recognized. This selection is based on the Poisson length dependencies found during training  204 . While selecting the beginning of frame  1  and the end of frame  12 , two separate probability scores are preferably used one for the start of the phoneme and one for the end of the phoneme with the obvious constraint that phoneme end occurs after the start of the phoneme. 
         [0061]    A search is made for maximizing a probability path  500  which successfully puts path  500  of each phoneme (e.g. for ‘aa’) in time order of the 3 or 2 sub-phonemes as constructed from the stored Gaussian mixture module probability states with Poisson length dependencies. The probability states are probed over the frames of the whole incoming speech buffer. Referring to  FIG. 5 , starting at sub-phoneme ‘aa 1 ’ block of frames, a series of probability peaks (for frames  1 - 4 ) is determined. Sub-phoneme ‘aa 2 ’ block of frames has probability peaks (4-9 frames). While probability drops (such as in the 2nd frame in ‘aa 2 ’ as marked by a dotted vertical line  302 , the overall probability is compensated by the the first sub-phoneme ‘aa 0 ’ in frame  6 . The decision rule for transferring to the next sub-phoneme ‘aa 2 ’ in order, is due to a probability drop of the current sub-phoneme ‘aa 1 ’, and an increasing probability of the next sub-phoneme ‘aa 2 ’ in order. A phoneme block is chosen as path  500  which successfully puts in time order the two or three 3 parts of the phoneme. 
         [0062]    The definite articles “a”, “an” is used herein, such as “a sub-phoneme”, “a probability density function” have the meaning of “one or more” that is “one or more sub-phonemes” or “one or more probability density functions”. 
         [0063]    Although selected embodiments of the present invention have been shown and described, it is to be understood the present invention is not limited to the described embodiments. Instead, it is to be appreciated that changes may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and the equivalents thereof.