Patent Publication Number: US-7590537-B2

Title: Speaker clustering and adaptation method based on the HMM model variation information and its apparatus for speech recognition

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   This application claims the priority of Korean Patent Application No. 2004-10663, filed on Feb. 18, 2004, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference. 
   BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   The present invention relates to a method and an apparatus for both speaker clustering and speaker adaptation based on the HMM model variation information. In particular, the present invention includes a method and an apparatus that yield an improved performance of automatic speech recognition in that it utilizes the average of model variation information over speakers. In addition, the present invention does not analyze only information on the quantity variation amount of model variation, but also analyzes information with respect to the directional variation amount. 
   2. Description of the Related Art 
   A speech recognition system is based on the correlation between speech and its characterization in an acoustic space for the speech. The characterization is typically obtained from training data. 
   The Speaker-Independent (SI) system is trained using a large amount of data acquired from a plurality of speakers, and acoustic model parameters are obtained as averages of speaker differences, yielding a limited modeling accuracy for each individual speaker. On the other hand, a Speaker-dependent (SD) system is trained by an adequate amount of speaker-specific data and shows a better performance than the SI system. However, the SD system has drawbacks in that collecting a sufficient amount of data for each single speaker, in order to properly train the acoustic models, is time consuming and unacceptable in many cases. As a compromise, a Speaker Adaptation (SA) system attempts to tune the available recognition system to a specific speaker to improve recognition performance while requiring only a little amount of speaker-specific data. 
     FIG. 1  illustrates a general speaker adaptation method that utilizes a Maximum Likelihood Linear Regression (MLLR) technique by which speaker adaptation may be achieved using a minimized amount of data. 
   If a speaker says, “It is said that it is going to rain today” (S 101 ), the utterance is converted into series of feature vectors, and then feature vectors are aligned with HMM states using the Viterbi alignment (S 103 ). Then, a class tree configured using characteristics of models in an acoustic model space is used (S 105 ), and a model transformation matrix is then estimated to transform the canonical model into a model suitable for a specific speaker (S 107 ). 
   Herein, the basic unit of each model is a subword. In the class tree, the base classes C 1 , C 2 , C 3  and C 4  are connected to upper nodes C 5  and C 6  according to their phonological or aggregative characteristics in the acoustic model space. Accordingly, although a node C 1  having data that are not sufficient to estimate a transformation matrix using a minimized number of utterances is generated, since a model of a cluster C 1  may be transformed using the transformation matrix estimated at the upper node C 5 , speaker adaptation may be achieved with a minimized number of data. 
   A class configuration method using a phonological knowledge base and aggregative characteristics of acoustic model space is suggested in C. J. Leggetter, “Improved Acoustic Modeling for HMMs using Linear Transform” Ph. D thesis, Cambridge University, 1996 “Regression Class Generation based on Phonetic Knowledge and Acoustic Space”. Such a method is, however, lacking in a mathematical basis and logic to support the hypothetical that phonemes of similar speech methods are located in a similar region in the acoustic model space. Additionally, there is a cluster difference between models before and after a speaker adaptation, but the method ignores the cluster difference. In other words, when clustering is performed using only a dispersion of models in an acoustic model space of a speaker-independent model before a speaker adaptation, models belonging to an arbitrary cluster may shift to other clusters after adapting to a speaker. Herein, since an identical parameter is applied to an identical cluster, speaker adaptation is resultantly performed in such shifted models by an erroneous transformation matrix. 
   In the meantime, the performance of a speaker adaptation system may be enhanced using a speaker clustering method for constituting acoustic models separately for each speaker group having a similar model dispersion in the acoustic model space. 
   U.S. Pat. No. 5,787,394, “State-dependent speaker clustering for speaker adaptation” discloses a speaker adaptation method that uses speaker clustering. According to the method of U.S. Pat. No. 5,787,394, the likelihood of all speaker models is analyzed when a speaker model cluster that is the most similar to a test speaker is selected. Thus, when the model similar to the test speaker model is not found in the selected speaker model cluster, a new prediction should be performed using another speaker cluster model. Accordingly, the amount of calculation is significant, and the calculation speed is also decreased. In addition, according to the method of U.S. Pat. No. 5,787,394, when a speaker model cluster that is most similar to a maximum likelihood (hereinafter, referred to as ML) model of a test speaker is selected, only a quantity variation amount is analyzed between the compared models, and the directional variation amount is disabled. Thus, even if the directional variation amounts are different from each other, if the quantity variation amounts are identical, the models may be bound in the same cluster. 
   SUMMARY OF THE INVENTION 
   The present invention relates to a method and an apparatus for both speaker clustering and speaker adaptation based on HMM model variation information. In particular, the present invention includes a method and an apparatus that yield an improved performance of automatic speech recognition in that it utilizes the average of model variation information over speakers. In addition, in analyzing the model variation, the present invention does not analyze only scalar information, but also analyzes vector information. 
   According to an aspect of the present invention, a speaker clustering method includes: extracting a feature vector from speech data of a plurality of training speakers; genera an ML (maximum likelihood) model of the feature vector for the plurality of training speakers; obtaining model variation information of the plurality of training speakers while analyzing the quantity variation amount and/or the directional variation amount in an acoustic space of the ML model with respect to a speaker-independent model; generating a plurality of speaker clusters by applying a predetermined clustering algorithm to the plurality of information on the model variation; and generating a transformation parameter to be used to generate a speaker adaptation model with respect to the speaker-independent model for the plurality of speaker group models. 
   Herein, the model variation is represented as Equation 1.
 
 D ( x,y ) =D   Eucledian ( x,y ) α (1−cos θ)  Equation 1
     where x is a vector of an ML model of a training speaker;   y is a vector of a speaker-independent model of a training speaker;   

   
     
       
         
           
             
               
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   Herein, α may be 0 or 1. 
   In addition, in extracting a feature vector from the speech data of a plurality of training speakers, a plurality of feature vectors may be extracted from the training speakers. In generating an ML model of the feature vector for the plurality of training speakers, the Viterbi alignment may be performed on the feature vector. 
   A speaker adaptation method further includes: applying a predetermined clustering algorithm to the plurality of ML models, and generating a plurality of speaker group ML models. The generation of the speaker adaptation model includes: extracting a feature vector from the speech data of a test speaker; generating a test speaker ML model for the feature vector; calculating the model variation between the test speaker ML model and a speaker group ML model to which the test speaker belongs, and selecting a speaker group model that is most similar to the calculated model among the plurality of speaker group models; applying a predetermined prediction algorithm to a variation parameter of the selected speaker group model variation, and predicting and generating an adaptation parameter; and applying the adaptation parameter to the speaker adaptation model. 
   The calculated model variation may be represented as in Equation 1. 
   According to another aspect of the present invention, a speaker clustering method includes: extracting a feature vector from the speech data of a plurality of training speakers; generating an ML model of the feature vector for the plurality of training speakers; generating the model variation of the plurality of training speakers while analyzing the quantity variation amount and/or the directional variation amount in an acoustic space of the ML model with respect to a speaker-independent model; generating a global model variation representative of all of the plurality of model variations; and generating a variation parameter to be used to generate a speaker adaptation model with respect to the speaker-independent model using the global model variation. 
   The calculated model variation may be represented as Equation 1, and the global model variation may be an average of the plurality of model variations. 
   According to another aspect of the present invention, a speech recognition apparatus includes: a feature extractor which extracts a feature vector from the speech data of a plurality of training speakers; a Viterbi aligner, which performs Viterbi alignment on the feature vector with respect to a speaker-independent model for the plurality of training speakers, and generates an ML model with respect to the feature vector; a model variation generator which generates a model variations of the plurality of training speakers while analyzing the quantity variation amount and/or the directional variation amount in an acoustic space of the ML model with respect to a speaker-independent model; a model variation clustering unit which generates a plurality of speaker group model variations by applying a predetermined clustering algorithm to the plurality of model variations on the basis of the likelihood of the model variation; and a variation parameter generator which generates a variation parameter to be used to generate a speaker adaptation model with respect to the speaker-independent model, for the plurality of speaker group model variations. 
   The model variation clustering unit further applies a predetermined clustering algorithm to the plurality of ML models and generates a plurality of speaker group ML models; the feature extractor extracts a feature vector from the speech data of a test speaker, and then the Viterbi aligner generates a test speaker ML model for the feature vector, thus generating the speaker adaptation model. Herein, the apparatus further includes: a speaker cluster selector which calculates a model variation between the test speaker ML model and a speaker group ML model to which the test speaker belongs and selects a speaker group model that is most similar to the calculated model variation among the plurality of speaker group model; and an adaptation parameter generator which applies a predetermined prediction algorithm to a variation parameter of the selected speaker group model variation, predicts an adaptation parameter, generates the adaptation parameter, and applies the adaptation parameter to the speaker adaptation model. 
   According to another aspect of the present invention, a speech recognition apparatus includes: a feature extractor which extracts a feature vector from the speech data of a plurality of training speakers; a Viterbi aligner which performs Viterbi alignment on the feature vector with respect to a speaker-independent model for the plurality of training speakers, and generates an ML model with respect to the feature vector; a model variation generator which generates model variation of the plurality of training speakers while analyzing the quantity variation amount and/or the directional variation amount in an acoustic space of the ML model with respect to a speaker-independent model; a model variation clustering unit which generates a global model variation representative of all of the plurality of model variations; and a variation parameter generator which generates a variation parameter to be used to generate a speaker adaptation model with respect to the speaker-independent model using the global model variation. 
   Additional aspects and/or advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     These and/or other aspects and advantages of the invention will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which: 
       FIG. 1  illustrates a general speaker adaptation system according to an MLLR algorithm; 
       FIG. 2  illustrates a speech recognition apparatus which implements speaker clustering according to an embodiment of the present invention; 
       FIGS. 3A and 3B  illustrate model variation according to another embodiment of the present invention; 
       FIG. 4  is a flowchart of a speaker clustering method according to yet another embodiment of the present invention; 
       FIG. 5  illustrates a speech recognition apparatus which implements speaker adaptation according to another embodiment of the present invention; 
       FIG. 6  is a flowchart of a speaker adaptation method according to another embodiment of the present invention; 
       FIG. 7  is a flowchart of a speech recognition method according to another embodiment of the present invention; 
       FIG. 8  is an example of an experiment according to another embodiment of the present invention; and 
       FIG. 9  is an example of an experiment according to another embodiment of the present invention. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   Reference will now be made in detail to the 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. 
   Embodiments of the present invention will be described referring to accompanied drawings. A speech recognition process is divided into speaker clustering, speaker adaptation and speech recognition. The speaker clustering will be described referring to  FIGS. 2 to 4 . The speaker adaptation will be will be described referring to  FIGS. 5 and 6 . The speech recognition will be described referring to  FIG. 7 . 
     FIG. 2  illustrates a speech recognition apparatus which implements speaker clustering according to an embodiment of the present invention. The speech recognition apparatus  20  includes a feature extractor  201 , a Viterbi aligner  203 , a model variation generator  205 , a model variation clustering unit  207  and a variation parameter generator  209 . The feature extractor  201  extracts a feature vector used to recognize speech from the speech data  231  of N-numbered training speakers. The Viterbi aligner  203  performs the Viterbi alignment on the extracted feature vector using a Viterbi algorithm, and generates an ML model  235  of each training speaker. The model variation generator  205  generates a model variation  237  of each training speaker from the difference between a speaker-independent model  233  and the ML model  235  of the training speaker. The model variation clustering unit  207  generates M-numbered model variation groups  239 - 1  from the speakers on the basis of a likelihood of the model variation  237  of the training speakers. The variation parameter generator  209  predicts variation parameters for the plurality of speaker groups  239 - 1 , and generates a variation parameter  239 - 2  for each speaker group. 
   The feature extractor  201  extracts a feature vector used to recognize speech. As the widely used feature vectors of a speech signal, there are feature vectors obtained by a linear predictive cepstrum (hereinafter, referred to as LPC) method, a mel frequency cepstrum (hereinafter, referred to as MFC) method, and a perceptual linear predictive (hereinafter, referred to as PLP) method. In addition, as the pattern recognition techniques for speech recognition, there are a dynamic time warping (hereinafter, referred to as DTW) technique and a neural network technique, which have problems which should be solved when applied to the recognition of significant amount of vocabulary. Accordingly, a speech recognition method using a hidden Markov model (hereinafter, referred to as HMM) is widely used today. As for HMM, many kinds of recognizers may be implemented from low capability to high capability, depending on model configurations only by setting a recognition unit according to a number of recognition words. 
   The Viterbi aligner  203  performs a Viterbi alignment on the feature vector for each training speaker using a Viterbi algorithm, and generates an ML model  235 . The Viterbi algorithm is used to optimize a search space. The Viterbi algorithm may readily be implemented by hardware. The Viterbi algorithm is suitable for fields wherein energy efficiency is important. Accordingly, in the speech recognition fields, the Viterbi algorithm is usually used to determine the optimal state sequence. In other words, the Viterbi aligner  203  obtains the state sequence, using the Viterbi algorithm, which has the highest probability that an observation sequence of the feature vector is observed. Additionally, the Viterbi aligner  203  generates an ML model in which model parameters of speaker-independent model are newly predicted using a maximum likelihood estimation obtained by the well-known Baum-Welch algorithm. Herein, since a database for the same speaker is necessary to train a speaker&#39;s speech, various feature vectors are extracted from database  231  of each training speaker and are Viterbi-aligned in this embodiment. Then, a new variable is introduced to the ML model of a single observation sequence and the ML models  235  of the observation sequences of the feature vectors, that is, multiple observation sequences are generated. 
   The model variation generator  205  generates a model variation  237  of each training speaker from the difference between the ML model  235  of the training speaker and the speaker-independent model  233  in an acoustic space. Herein, the difference between models in the acoustic space is obtained when analyzing both the quantity variation amount and the directional variation amount. The speaker-independent model  233  is deliberately prepared before speaker adaptation, and represents an average trend for all the speakers. The speaker-independent model  233  may be a single model and may also be converted into a multiple model by clustering speakers according to sex, age and province. 
   As shown  FIG. 3A , the quantity variation amount represents a Euclidian distance between a speaker-independent model A or B and an ML model A′ and B′ of a training speaker. The directional variation amount represents an angular variation amount of the acoustic space between the speaker-independent model A or B and the ML model A′ and B′ of the training speaker, and is represented by Equation 1.
 
 D ( x,y ) =D   Euclidian ( x,y ) α (1−cos θ)  Equation 1
     where x is a vector of an ML model of a training speaker;   y is a vector of a speaker-independent model of a training speaker;   

   
     
       
         
           
             
               
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   In other words, the difference between the speaker-independent model  233 , and the ML model  237  of the training speaker causes model variation  237  according to Equation 1. 
   The model variation clustering unit  207  clusters the speakers into M-numbered model variation groups  239 - 1  from the speakers on the basis of a likelihood of the model variations  237  of the N-numbered training speakers. Herein, Equation 1 is used to determine model variations. As a clustering algorithm, the well-known Linde-Buzo-Gray (hereinafter, referred to as LBG) algorithm or K-means algorithm may be used. Meanwhile, although it is not separately shown that an acoustic characteristic is clear, the model variation clustering unit  207  generates M-numbered speaker group ML models corresponding to M-numbered speaker groups  239 - 1  in pairs from N-numbered ML models  235  of training speaker using clustering information for N-numbered model variations  237  of the training speaker. This speaker group ML model is used in the speaker adaptation method described later. 
   The variation parameter generator  209  predicts the variation parameters for the plurality of speaker groups  239 - 1  according to an MLE method, and generates a variation parameter  239 - 2  corresponding to each speaker group  239 - 1 . The variation parameter  239 - 2  is used to predict an adaptation parameter when a speaker adaptation model is generated from a speaker-independent model in a speaker adaptation process to be described later. Herein, with respect to the variation parameters, the variation parameter generator  209  configures a priori-probability in the case of a maximum a posteriori (hereinafter, referred to as MAP) and a class tree in the case of maximum likelihood linear regression (hereinafter, referred to as MLLR) according to the speaker adaptation algorithm. 
   Then, referring to  FIG. 2 , a speaker clustering method according to another embodiment of the present invention shown in  FIG. 4  will be described. In  FIG. 4 , feature vectors are extracted from the speech data  231  of N-numbered training speakers (S 401 ). Then, a Viterbi alignment is performed on the feature vectors by a Viterbi algorithm (S 403 ). An ML model  235  of the feature vector is generated from the feature vector for each training speaker (S 405 ). Model variations of the training speakers are generated while analyzing the quantity variation amount and/or the directional variation amount from a speaker-independent model  233  to the ML model  235  of the training speaker (S 407 ). The training speakers are clustered into M-numbered speaker groups  239 - 1  according to the model variation represented as Equation 1 (S 409 ). Finally, a variation parameter is generated for each speaker group  239 - 1  (S 411 ). Accordingly, the speaker clustering is completed according to the  FIG. 2  embodiment of the present invention. M-numbered speaker group ML models corresponding to M-numbered speaker groups  329 - 1  in pairs are generated from ML models  235  of N-numbered training speakers using clustering information of S 409 . This speaker group ML model is used in a speaker adaptation method described later. 
     FIG. 5  illustrates a speech recognition apparatus which implements a speaker adaptation according to another embodiment of the present invention. Referring to  FIG. 5 , the speaker adaptation process using M-numbered speaker group model variations and corresponding variation parameters  239 - 2  generated according to  FIGS. 2 and 4  will be described. 
   A speech recognition apparatus  50  includes a feature extractor  501 , a Viterbi aligner  503 , a model variation generator  505 , an adaptation parameter predictor  507  and a speech recognizer  509 . The feature extractor  501  extracts a feature vector used to recognize speech from a test speaker. The Viterbi aligner  503  performs a Viterbi alignment on the extracted feature vector with respect to parameters of a speaker-independent model  511  according to a Viterbi algorithm in a speech space, and generates an ML model of the test speaker of the feature vector. The model variation generator  505  calculates a model variation between the test speaker ML model and a speaker group ML model  513  to which the test speaker belongs, and selects a speaker group that has a speaker group model variation that is most similar to the calculated model variation among the speaker groups  239 - 1 . The adaptation parameter predictor  507  applies an MLE method to a variation parameter of the selected speaker group model variation and predicts an adaptation parameter. The speech recognizer  509  outputs a feature vector of the speech of the speaker in a sentence referring to the speaker adaptation model  519  and the vocabulary dictionary  521 . 
   The feature extractor  501  extracts a feature vector used to recognize speech. As the widely used feature vectors of a speech signal, there are feature vectors obtained by an LPC method, an MFC method and a PLP method. 
   The Viterbi aligner  503  performs Viterbi alignment on the feature vectors with respect to parameters of the speaker-independent model  511  according to a Viterbi algorithm, and generates ML models of the feature vectors. The speaker-independent model  511  is deliberately prepared before the speaker adaptation. It represents an average trend for all the speakers. The speaker-independent model  511  may cluster speakers according to sex, age and province. The speaker cluster selector  505  selects the speaker group model variation  513 . Then, the Viterbi aligner  503  performs a speaker group model variation  513 , a Viterbi alignment and an ML prediction on the test speaker ML model. 
   The speaker cluster selector  505  calculates model variation between the test speaker ML model and the speaker group ML model (generated when clustering speakers referring to  FIGS. 2 and 4 ) to which the test speaker belongs, and selects a speaker group that has a speaker group model variation that is most similar to the calculated model variation among the speaker groups  239 - 1 . Herein, the speaker cluster selector  505  measures the likelihood of a model variation while analyzing both the directional variation amount and quantity variation amount according to Equation 1 so as to select a speaker group. Herein, the speaker cluster selector  505  provides the Viterbi aligner  503  and the adaptation parameter predictor  507  with the model variation  513  of the selected speaker group, and provides the adaptation parameter predictor  507  with the variation parameter  515  of the selected speaker group. 
   The adaptation parameter predictor  507  predicts the adaptation parameter from the variation parameter  515  of the selected speaker group on the basis of the alignment result of the Viterbi aligner and the model variation  513  of the selected speaker group, and applies the adaptation parameter to the speaker adaptation model  519 . Accordingly, the parameters of the speaker adaptation model are transformed in the acoustic space by the adaptation parameter. Then, the adaptation parameter predictor  507  repeats the process of receiving the speech from a test speaker, predicting an adaptation parameter and applying the adaptation parameter to the speaker adaptation model. When the speaker adaptation is completed, the speaker recognizer  509  outputs the input speech of the test speaker in a sentence referring to a language model  517 , a speaker adaptation model  519 , and the vocabulary dictionary. 
   For example, in the case of MAP, priori probability is obtained using an expectation maximization (hereinafter, referred to as EM) algorithm so that the difference between the limited training data (speaker adaptation registration data) and the existing speaker-independent model is minimized, and then the limited training data is applied to speaker adaptation model using the obtained priori probability. In the case of MLLR, a variation matrix that matches the existing speaker-independent model to the speaker using the limited training data (speaker adaptation registration data) is predicted, and then the limited training data is transformed into a speaker adaptation model using the predicted variation matrix. 
   In the meantime, the language model  517 , the speaker adaptation model  519  and the vocabulary dictionary  521  are obtained beforehand in a learning process. The language model  517  has a bigram or trigram occurrence probability data of a word sequence operated using occurrence frequency data for a word sequence of learning sentences constructed in a learning text database. The learning text database may consist of sentences that may be used to recognize speech. The speaker adaptation model  519  generates acoustic models such as a hidden Markov model (hereinafter, referred to as HMM) using the feature vectors of the speaker extracted from the speech data of the learning speech database. The acoustic models are used as reference models in a speech recognition process. Since a recognition unit, to which a phonological change is applied, should be processed, the vocabulary dictionary  521  is a database in which all the pronunciation representations, including a phonological change are included for all the headwords. 
     FIG. 6  is a flowchart of a speaker adaptation method according to another embodiment of the present invention. Referring to  FIG. 6 , feature vectors used to recognize the words of speech are extracted from the speech data of a test training speaker (S 601 ). Then, the feature vectors are aligned for ML with respect to the parameters of the speaker-independent model  511  according to the Viterbi algorithm, and the ML model of the test speaker is generated (S 603 , S 605 ). Then, a model variation is measured while analyzing both the quantity variation amount and the directional variation amount of the model according to Equation 1, and then a speaker group  513  and the variation parameter of the speaker group  513  are selected (S 607 ). Then, the adaptation parameter is predicted and generated from the variation parameter  515  of the selected speaker group on the basis of the Viterbi alignment result and model variation  513  of the selected speaker group L (S 609 ), and then the generated adaptation parameter is applied to the speaker adaptation model (S 613 ). 
     FIG. 7  is a flowchart of a speech recognition method according to another embodiment of the present invention. When the speaker clustering based on a model variation described referring to  FIGS. 2 to 4  (S 702 ) and the speaker adaptation based on a model variation described referring to  FIGS. 5 and 6  (S 704 ) are performed and a speaker adaptation is completed, speech is received from a speaker, and the sentence corresponding to the received speech is outputted (S 706 ). 
   Referring to  FIGS. 8 and 9 , experimental results according to the embodiment of the present invention will be described.  FIG. 8  is a result of an experiment according to another embodiment of the present invention in which a model variation is generated for each of the training speakers while analyzing the quantity variation amount and the directional variation amount according to Equation 1 without applying speaker clustering to this embodiment, and speaker adaptation is performed using the model variations. 
   Accordingly, a model variation clustering unit  207  does not generate a speaker cluster, but generates a global model variation representative of N-numbered training speaker model variations  237 . Herein, the global model variations may be an average of all the training speaker model variations  237 . For example, the number of models is K. N-numbered training speaker model variations are speaker  1 ={d 1 _ 1 , d 1 _ 2 , d 1 _ 3 , . . . , d 1 _K}, speaker  2 ={d 2 _ 1 , d 2 _ 2 , d 2 _ 3 , . . . , d 2 _K}, . . . , speaker N={dN_ 1 , dN_ 2 , dN_ 3 , . . ., dN_K}, where d is the difference between the speaker-independent model and the ML model for the speakers. Herein, the global model variation may be represented as {m 1 , m 2 , m 3 , . . . , mk} where m 1 =d 1 _ 1 +d 2 _ 1 +d 3 _ 1 + . . . +dN_ 1 )/N, m 2 =(d 1 _ 2 +d 2 _ 2 +d 3 _ 2 + . . . +dN_ 2 )/N, . . . , mk=(d 1 _k+d 2 _k+d 3 _k+ . . . +dN_k)/N. In addition, a variation parameter generator  209  predicts a variation parameter to be used to generate a speaker adaptation model using the global model variation and generates the variation parameter according to the MLE method. 
   Meanwhile, the description of the model variation clustering unit  207  will be omitted. Instead, the model variations  237  of N-numbered training speakers may have N-numbered corresponding variation parameters instead of a speaker group variation parameter  239 - 2 . Herein, the speaker cluster selector  505  of  FIG. 5  selects a model variation of a specific training speaker directly from model variations  237  and corresponding variation parameters of N-numbered training speakers. The adaptation parameter predictor  507  then generates an adaptation parameter on the basis of a model variation of this specific training speaker. 
   In the experiment, the speech data obtained by reading a colloquial sentence as narration were used. A total of 4,500 speech sentences were used as the experimental data, the speech sentences including 1,500 adaptation speeches for speaker adaptation and 3,000 test speeches for experiment. Fifty adaptation speech sentences and one hundred test speech sentences were obtained from fifteen men and fifteen women. Each speech sentence was collected using a Sennheizer MD431 unidirectional microphone in a quiet office environment. Additionally, MLLR was used as an adaptation algorithm. The training speakers constituting a model included twenty-five men and twenty-five women. The number of base classes constituting the lowest layer in the class tree of each model is sixty-four. 
   In the meanwhile, referring to  FIG. 8 , comparative examples 1 and 2 are a phonological knowledge based speaker adaptation model and a location likelihood based speaker adaptation model. The weights of experimental examples 1, 2 and 3 are given as α, 0 and 1. A word error rate (hereinafter, referred to as WER) is used generally to measure an error rate in speech recognition. The relative WER reduction rate represents how much the error rate is reduced in comparison with the WER of speaker-independent model. 
   As shown in  FIG. 8 , even in the case that the speaker adaptation is performed without clustering the speaker, the WERs (%) of the experimental examples 1, 2 and 3 are 2.94, 2.78 and 2.79, respectively, and represent the relative WER reduction rate of 26.1%, 30.2% and 29.9% in comparison with the speaker-independent WER, respectively. In comparison with the existing speaker adaptation method suggested as a comparative example, it was found that the relative WER reduction rate was improved by about 10%. Generally, in a speech recognition apparatus having more than 95%, this recognition performance improvement is significant when the relative difficulty of recognition performance improvement is taken into account. Herein, note that the WER was more improved in the case in which only the directional variation amount was analyzed (2.78%, 2.79%) rather than the case in which only quantity variation amount was analyzed (2.94%). Accordingly, the directional variation amount is a more significant factor in the speech recognition performance. 
   Meanwhile, as for the experimental example 2 shown in  FIG. 8 , in the experimental examples 4 and 5 in which a speaker is clustered with eight and sixteen speaker clusters, the WER and relative WER reduction rate have greater improvements than the rates obtained using the comparative examples 1 and 2, as shown in  FIG. 9 . 
   The speaker clustering method and the speaker adaptation method may be implemented by programs stored on a computer readable recording medium. The recording medium includes a carrier wave, such as transmission through the Internet, as well as an optical recording medium and a magnetic recording medium. 
   According to the present invention, in measuring a model variation, the directional variation amount, as well as a quantity variation amount, is analyzed so that the speaker cluster accuracy is improved. 
   According to the present invention, in measuring a model variation likelihood when the speaker cluster is selected, the directional variation amount, as well as the quantity variation amount, is analyzed so that the accuracy of the speaker cluster selection is improved. 
   According to the present invention, when a model variation is measured, both the quantity variation amount and the directional variation amount are analyzed so that the error rate of speech recognition is drastically lowered. 
   The invention may also be embodied as computer readable codes on a computer readable recording medium. The computer readable recording medium is any data storage device that may store data which may be thereafter read by a computer system. Examples of the computer readable recording medium include read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, and optical data storage device. The computer readable recording medium may also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. 
   Although a few embodiments of the present invention have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the claims and their equivalents.