Abstract:
The present invention relates to a method for modeling a common-language speech recognition, by a computer, under the influence of multiple dialects and concerns a technical field of speech recognition by a computer. In this method, a triphone standard common-language model is first generated based on training data of standard common language, and first and second monophone dialectal-accented common-language models are based on development data of dialectal-accented common languages of first kind and second kind, respectively. Then a temporary merged model is obtained in a manner that the first dialectal-accented common-language model is merged into the standard common-language model according to a first confusion matrix obtained by recognizing the development data of first dialectal-accented common language using the standard common-language model. Finally, a recognition model is obtained in a manner that the second dialectal-accented common-language model is merged into the temporary merged model according to a second confusion matrix generated by recognizing the development data of second dialectal-accented common language by the temporary merged model. This method effectively enhances the operating efficiency and admittedly raises the recognition rate for the dialectal-accented common language. The recognition rate for the standard common language is also raised.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a method, a system and a program for modeling a common-language speech recognition, by a computer, under the influence of multiple dialects, and also relates to a recording medium that stores the program. The present invention particularly relates to a field of speech recognition by a computer. 
     2. Description of the Related Art 
     Enhancing robustness has been an important issue and a difficult point to achieve in the field of speech recognition. A major factor of deterioration in robustness of speech recognition lies in a problem involving linguistic accents. For example, the Chinese language has many dialects, which leads to a significant problem of accents. The problem gives incentives for ongoing research activities. In the conventional speech recognition system, the recognition rate for a standard common language is high but the recognition rate for an accented common language influenced by dialects (hereinafter referred to as “dialectal-accented common language” or simply as “dialectal common language” also) is low. To address this problem, a method such as “adaptation” may be used as a countermeasure in general. However, a precondition in this case is that a sufficient amount of data for the dialectal-accented common language must be provided. With this method, there are cases where the recognition rate for the standard common language drops markedly. Since there are many kinds of dialects, the work efficiency is degraded if an acoustic model is trained repeatedly for the respective kinds of dialects. 
     SUMMARY OF THE INVENTION 
     The present invention has been made in view of the foregoing problems, and one of purposes is to provide a method for modeling a common language speech recognition, by a computer, under the influence of a plurality of dialects, the method being capable of raising the recognition rate for dialectal-accented common languages with a small amount of data and guaranteeing to sustain the recognition rate for the standard common language, and to provide a system therefor. 
     A method, for modeling a common-language speech recognition by a computer under the influence of a plurality of dialects, includes the following steps of: 
     (1) generating a triphone standard common-language model based on training data of standard common language, generating a first monophone dialectal-accented common-language model based on development data of dialectal-accented common language of first kind, and generating a second monophone dialectal-accented common-language model based on development data of dialectal-accented common language of second kind; 
     (2) generating a first confusion matrix by recognizing the development data of the dialectal-accented common language of first kind using the standard common-language model, and obtaining a temporary merged model in a manner that the first dialectal-accented common-language model is merged into the standard common-language model according to the first confusion matrix; and 
     (3) generating a second confusion matrix by recognizing the development data of the dialectal-accented common language of second kind using the temporary merged model, and obtaining a recognition model in a manner that the second dialectal-accented common-language model is merged into the temporary merged model according to the second confusion matrix. 
     The merging method as described in the above steps (2) and (3) is such that: 
     a probability density function of the temporary merged model is expressed by
 
 p ′( x|s )=λ 1   p ( x|s )+(1−λ 1 ) p ( x|d   1 ) p ( d   1   |s )
 
where x is an observation feature vector of speech to be recognized, s is a hidden Markov state in the standard common-language model, d 1  is a hidden Markov state in the first dialectal-accented common-language model, and λ 1  is a linear interpolating coefficient such that 0&lt;λ 1 &lt;1, and
 
     wherein a probability density function of the merged recognition model is expressed by 
                 p   ′′     ⁡     (     x   |   s     )       =         ∑     k   =   1     K     ⁢       w   k       (   sc   )     ′       ⁢       N   k     (   sc   )       ⁡     (   ·   )           +       ∑     m   =   1     M     ⁢       ∑     n   =   1     N     ⁢       w   mn       (     dc   ⁢           ⁢   1     )     ′       ⁢       N   mn     (     dc   ⁢           ⁢   1     )       ⁡     (   ·   )             +       ∑     p   =   1     P     ⁢       ∑     q   =   1     Q     ⁢       w   pq       (     dc   ⁢   2     )     ′       ⁢       N   pq     (     dc   ⁢           ⁢   2     )       ⁡     (   ·   )                     
where w k   (sc)′  is a mixture weight for the hidden Markov state of the standard common-language model, w mn   (dc1)′  is a mixture weight for the hidden Markov state of the first dialectal-accented common-language model, w pq   (dc2)′  is a mixture weight for the hidden Markov state of the second dialectal-accented common-language model, K is the number of Gaussian mixtures for Hidden Markov Model state s in the standard common-language model, N k   (sc) (•) is an element of Gaussian mixture for Hidden Markov Model state s, M is the number of d 1  that is considered as the pronunciation variants occurring between the first dialectal-accented common-language model for d 1  and the standard common-language-model, N is the number of Gaussian mixtures for Hidden Markov Model state d 1  in the first dialectal-accented common-language model, N mn   (dc1) (•) is an element of Gaussian mixture for Hidden Markov Model state d 1 , P is the number of d 2  that is considered as the pronunciation variants occurring between the second dialectal-accented model for d 2  and the standard common-language model, Q is the number of Gaussian mixtures for Hidden Markov Model state d 2  in the second dialectal-accented model, N pq   (dc2) (•) is an element of Gaussian mixture for Hidden Markov Model state d 2 .
 
     The method, for modeling a common-language speech recognition by a computer under the influence of a plurality of dialects, according to the above embodiment achieves the following advantageous effects. 
     Each of a plurality of dialectal-accented common models is merged into a standard common-language model using an iterative method, so that the redundant operation of training an acoustic model for each of dialects can be avoided and therefore the work efficiency can be enhanced. Also, according to this method, the recognition rate for dialectal-accented common languages can be admittedly raised. At the same time, the recognition rate for the standard common language never deteriorates and sometimes increases. Thus, this method resolves a problem, as in other conventional methods, where the recognition rate for the standard common language markedly deteriorates while a dialectal-accented common language is properly treated. 
     Optional combinations of the aforementioned processes, and implementations of the invention in the form of apparatuses, systems, recoding media, computer programs and so forth may also be practiced as additional modes of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments will now be described by way of examples only, with reference to the accompanying drawings which are meant to be exemplary, not limiting, and wherein like elements are numbered alike in several Figures in which: 
         FIG. 1  conceptually shows a principle of a modeling method according to an embodiment; and 
         FIG. 2  is a block diagram showing an example of a modeling system that realizes a modeling method as shown in  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A description is now given of preferred embodiments of the present invention with reference to drawings. 
       FIG. 1  conceptually shows the principle of a method for modeling a speech recognition of common language under the influence of an n kinds of dialects (n being an integer greater than or equal to 2) according to an embodiment of the present invention. This modeling method includes the following three steps of: 
     (1) generating a triphone standard common-language model based on training data of standard common language, and generating first to nth monophone dialectal-accented common-language models for respective corresponding dialectal-accented common languages of first to nth kinds, based on the development data thereof; 
     (2) generating a first confusion matrix by recognizing the development data of the dialectal-accented common language of first kind using the standard common-language model, and obtaining a first temporary merged model in a manner that the first dialectal-accented common-language model is merged into the standard common-language model according to the first confusion matrix; and 
     (3) generating an ith confusion matrix by recognizing the development data of dialectal-accented common language of ith kind using an (i−1)th temporary merged model (i being an integer such that 2≦in≦), and obtaining a final recognition model by repeating, from i=2 to i=n, an operation of merging the ith dialectal-accented common-language model into the (i−1)th temporary merged model according to the ith confusion matrix. 
       FIG. 2  is a block diagram showing a system for modeling the aforementioned speech recognition of a common language under the influence of a plurality of dialects. A modeling system according to the present embodiment comprises a model generation unit  100  and a control unit  200 . Referring to  FIG. 2 , the model generation unit  100  includes training database (hereinafter abbreviated as “training DB” also)  10 - 0 , development databases (hereinafter abbreviated as “development DB” also)  10 - 1  to  10 - n , model generators  30 - 0  to  30 - n , confusion matrix generators  40 - 1  to  40 - n , and model merging units  50 - 1  to  50 - n.    
     The training DB  10 - 0  is a database that stores the training data of a standard common language. 
     The development DB  10 - 1  to  10 - n  are databases that store the development data of dialectal-accented common languages of first to nth kinds, respectively. 
     The model generator  30 - 0  is used to generate a triphone standard common-language model based on the training data of the standard common language stored in the training DB  10 - 0 . 
     The model generators  30 - 1  to  30 - n  are a sequence of blocks that generate first to nth monophone dialectal-accented common-language models based on the training data of dialectal-accented standard common languages of first to nth kinds stored in the development databases  10 - 1  to  10 - n , respectively. 
     The confusion matrix generators  40 - 1  to  40 - n  are a sequence of blocks that generate first to nth confusion matrices by recognizing the development data of the first to nth dialectal-accented common languages of first to nth kinds stored in the first to nth development databases  10 - 1  to  10 - n  using the models generated by the corresponding model generators  30 - 0  to  30 -( n - 1 ). 
     The model merging unit  50 - 1  generates a first temporary merged model in a manner that the first dialectal-accented common-language model generated by the model generator  30 - 1  is merged into a standard common-language model generated by the model generator  30 - 0  according to the first confusion matrix generated by the confusion matrix generator  40 - 1 . 
     The model merging units  50 - 2  to  50 -( n - 1 ) generate second to (n−1)th temporary merged models in a manner that the second to (n−1)th dialectal-accented common-language models generated by the model generators  30 - 2  to  30 -( n - 1 ) are each merged into a temporary merged model generated by a model merging unit placed immediately prior thereto according to the second to (n−1)th confusion matrices generated by the corresponding confusion matrix generators  40 - 2  to  40 -( n - 1 ). 
     The model merging unit  50 - n  finally generates a recognition model in a manner that the nth dialectal-accented common-language model generated by the model generator  30 - n  is merged into the (n−1)th temporary merged model generated by the model merging unit  50 -( n - 1 ) placed immediately prior thereto according to the nth confusion matrix generated by the confusion matrix generator  40 - n.    
     The control unit  200  controls the model generation unit  100  in such a manner as to operate according to the aforementioned modeling method. 
     In  FIG. 2 , the training DB  10 - 0  and the development DBs  10 - 1  to  10 - n  are depicted as separate blocks. However, they may be configured as a single database or a plurality of databases that store training data of a standard common language, development data of dialectal-accented common languages of first to nth kinds. Also, the model generators  30 - 0  to  30 - n  are depicted as separate blocks in  FIG. 2  but they may be configured as a single entity or a plurality of model generators and the single or plurality of model generators may be used in a time sharing manner, based on a control performed by the control unit  200 . Although the confusion matrix generators  40 - 1  to  40 - n  are depicted as separate blocks in  FIG. 2 , they may be configured as a single entity or a plurality of confusion matrix generators and the single or plurality of confusion matrix generators may be used in a time sharing manner, based on a control performed by the control unit  200 . Although the model merging units  50 - 1  to  50 - n  are depicted as separate blocks in  FIG. 2 , they may be configured as a single entity or a plurality of model merging units and the single or plurality of model merging units may be used in a time sharing manner, based on a control performed by the control unit  200 . 
     A concrete description is hereinbelow given of a method for modeling a recognition model capable of being compatible with two different kinds of dialectal-accented common languages (n=2). 
     This modeling method includes the following steps of: 
     (1) generating a triphone standard common-language model based on training data of standard common language, generating a first monophone dialectal-accented common-language model based on development data of dialectal-accented common language of first kind, and generating a second monophone dialectal-accented common-language model based on development data of dialectal-accented common language of second kind; 
     (2) acquiring a first confusion matrix by recognizing the development data of the dialectal-accented common language of first kind using the standard common-language model, and obtaining a temporary merged model in a manner that the first dialectal-accented common-language model is merged into the standard common-language model according to the first confusion matrix; and 
     (3) acquiring a second confusion matrix by recognizing the development data of the dialectal-accented common language of second kind using the temporary merged model, and obtaining a recognition model in a manner that the second dialectal-accented common-language model is merged into the temporary merged model according to the second confusion matrix. 
     The merging method as described in the above steps (2) and (3) is such that: 
     the probability density function of the temporary merged model is expressed by
 
 p ′( x|s )=λ 1   p ( x|s )+(1−λ 1 ) p ( x|d   1 ) p ( d   1   |s )
 
where x is an observation feature vector of speech to be recognized, s is a hidden Markov state in the standard common-language model, d 1  is a hidden Markov state in the first dialectal-accented common-language model, and λ 1  is a linear interpolating coefficient such that 0&lt;λ 1 &lt;1.
 
     Also, the probability density function of the recognition model is expressed by 
                 p   ′′     ⁡     (     x   |   s     )       =         ∑     k   =   1     K     ⁢       w   k       (   sc   )     ′       ⁢       N   k     (   sc   )       ⁡     (   ·   )           +       ∑     m   =   1     M     ⁢       ∑     n   =   1     N     ⁢       w   mn       (     dc   ⁢           ⁢   1     )     ′       ⁢       N   mn     (     dc   ⁢           ⁢   1     )       ⁡     (   ·   )             +       ∑     p   =   1     P     ⁢       ∑     q   =   1     Q     ⁢       w   pq       (     dc   ⁢   2     )     ′       ⁢       N   pq     (     dc   ⁢           ⁢   2     )       ⁡     (   ·   )                     
where w k   (sc)′  is a mixture weight for the hidden Markov state of the standard common-language model, w mn   (dc1)′  is a mixture weight for the hidden Markov state of the first dialectal-accented common-language model, w pq   (dc2)′  is a mixture weight for the hidden Markov state of the second dialectal-accented common-language model, K is the number of Gaussian mixtures for Hidden Markov Model state s in the standard common-language model, N k   (sc) (•) is an element of Gaussian mixture for Hidden Markov Model state s, M is the number of d 1  that is considered as the pronunciation variants occurring between the first dialectal-accented common-language model for d 1  and the standard common-language-model, N is the number of Gaussian mixtures for Hidden Markov Model state d 1  in the first dialectal-accented common-language model, N mn   (dc1) (•) is an element of Gaussian mixture for Hidden Markov Model state d 1 , P is the number of d 2  that is considered as the pronunciation variants occurring between the second dialectal-accented model for d 2  and the standard common-language model, Q is the number of Gaussian mixtures for Hidden Markov Model state d 2  in the second dialectal-accented model, N pq   (dc2) (•) is an element of Gaussian mixture for Hidden Markov Model state d 2 .
 
     The method according to the present embodiment is characterized by the features that models created based on various kinds of dialectal-accented data are merged into the standard common-language model in an iterative manner. The fundamental flow of this method is illustrated in  FIG. 1 . In the case of merging two different dialectal-accented common models and standard common-language model using the flow in  FIG. 1 , the probability density function of a temporary merged model can be expressed by
 
 p ′( x|s )=λ 1   p ( x|s )+(1−λ 1 ) p ( x|d   1 ) p ( d   1   |s ).
 
     In the above equation, X is an observation feature vector of speech to be recognized, s is a hidden Markov state in the standard common-language model, d 1  is a hidden Markov state in the first dialectal-accented common-language model. λ 1  is a linear interpolating coefficient such that 0&lt;λ 1 &lt;1, and indicates a mixture weight in the temporary merged model. In the actual setting, the optimum λ 1  is determined through experiments. p(d 1 |s) is the output probability of the hidden Markov state in the first dialectal-accented common-language model given the corresponding hidden Markov state in the standard common-language model and indicates a variation of pronunciations in the dialect of first kind relative to the standard common language. For the same reasoning, the probability density function of the final merged model may be expressed by 
                       p   ′′     ⁡     (     x   |   s     )       =       ⁢         λ   2     ⁢       p   ′     ⁡     (     x   |   s     )         +       (     1   -     λ   2       )     ⁢     p   ⁡     (     x   |     d   2       )       ⁢       p   ′     ⁡     (       d   2     |   s     )                       =       ⁢         λ   2     ⁢     λ   1     ⁢     p   ⁡     (     x   |   s     )         +         λ   2     ⁡     (     1   -     λ   1       )       ⁢     p   ⁡     (     x   |     d   1       )       ⁢     p   ⁡     (       d   1     |   s     )         +                     ⁢       (     1   -     λ   2       )     ⁢     p   ⁡     (     x   |     d   2       )       ⁢       p   ′     ⁡     (       d   2     |   s     )                     =       ⁢         λ   2     ⁢     λ   1     ⁢       ∑     k   =   1     K     ⁢       w   k     (   sc   )       ⁢       N   k     (   sc   )       ⁡     (   ·   )             +         λ   2     ⁡     (     1   -     λ   1       )       ⁢       ∑     m   =   1     M     ⁢       P   ⁡     (       d     1   ⁢   m       |   s     )       ·                           ⁢         ∑     n   =   1     N     ⁢       w   mn     (     dc   ⁢           ⁢   1     )       ⁢       N   mn     (     dc   ⁢           ⁢   1     )       ⁡     (   ·   )           +       (     1   -     λ   2       )     ⁢       ∑     p   =   1     P     ⁢       P   ⁡     (       d     2   ⁢   p       |   s     )       ·                           ⁢       ∑     q   =   1     Q     ⁢       w   pq     (     dc   ⁢           ⁢   2     )       ⁢       N   pq     (     dc   ⁢           ⁢   2     )       ⁡     (   ·   )                       =       ⁢         ∑     k   =   1     K     ⁢       λ   2     ⁢     λ   1     ⁢     w   k     (   sc   )       ⁢       N   k     (   sc   )       ⁡     (   ·   )           +       ∑     m   =   1     M     ⁢       ∑     n   =   1     N     ⁢         λ   2     ⁡     (     1   -     λ   1       )       ·                           ⁢           P   ⁡     (       d     1   ⁢   m       |   s     )       ·     w   mn     (     dc   ⁢           ⁢   1     )         ⁢       N   mn     (     dc   ⁢           ⁢   1     )       ⁡     (   ·   )         +       ∑     p   =   1     P     ⁢       ∑     q   =   1     Q     ⁢       (     1   -     λ   2       )     ·                           ⁢         P   ⁡     (       d     2   ⁢   p       |   s     )       ·     w   pq     (     dc   ⁢           ⁢   2     )         ⁢       N   pq     (     dc   ⁢           ⁢   2     )       ⁡     (   ·   )                     =       ⁢         ∑     k   =   1     K     ⁢       w   k       (   sc   )     ′       ⁢       N   k     (   sc   )       ⁡     (   ·   )           +       ∑     m   =   1     M     ⁢       ∑     n   =   1     N     ⁢       w   mn       (     dc   ⁢           ⁢   1     )     ′       ⁢       N   mn     (     dc   ⁢           ⁢   1     )       ⁡     (   ·   )             +                     ⁢       ∑     p   =   1     P     ⁢       ∑     q   =   1     Q     ⁢       w   pq       (     dc   ⁢           ⁢   2     )     ′       ⁢       N   pq     (     dc   ⁢           ⁢   2     )       ⁡     (   ·   )                         
where d 2  is a hidden Markov state in the second dialectal-accented common-language model, λ 2  is a linear interpolating coefficient such that 0&lt;λ 2 &lt;1, and indicates a mixture weight in the final merged model. In the actual setting, the optimum λ 2  is determined through experiments. K is the number of Gaussian mixtures for Hidden Markov Model state s in the standard common-language model. N k   (sc) (•) is an element of Gaussian mixture for Hidden Markov Model state s. M is the number of d 1  that is considered as the pronunciation variants occurring between the first dialectal-accented common-language model for d 1  and the standard common-language-model; N is the number of Gaussian mixtures for Hidden Markov Model state d 1  in the first dialectal-accented common-language model. N mn   (dc1) (•) is an element of Gaussian mixture for Hidden Markov Model state d 1 . P(d 1m |s) is the corresponding probability of pronunciation modeling. P is the number of d 2  that is considered as the pronunciation variants occurring between the second dialectal-accented model for d 2  and the standard common-language model; Q is the number of Gaussian mixtures for Hidden Markov Model state d 2  in the second dialectal-accented model. N pq   (dc2) (•) is an element of Gaussian mixture for Hidden Markov Model state d 2 . P(d 2p |s) is the corresponding probability of pronunciation model.
 
     It is easy to see from the last line of the above equation that the final merged model is actually constructed by taking the weighted sum of the standard common model, the first dialectal-accented model and the second dialectal-accented model. w k   (sc)′ , w mn   (dc1)′  and w pq   (dc2)′  indicate the mixture weights of three models represented by the above equation. Since the confusion matrices P(d 1m |s) and P(d 2p |s) and the interpolating coefficients λ 1  and λ 2  are already known, the weights for the mixture of normal distributions of three models can be easily determined. 
     A description is now given of exemplary embodiments: 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 (Description of experimental data) 
               
             
          
           
               
                 Data set 
                 Database 
                 Details 
               
               
                   
               
               
                 Training set of 
                 Training data of 
                 120 speakers, 200 
               
               
                 standard common 
                 standard common 
                 long sentences per 
               
               
                 language 
                 language 
                 speaker 
               
               
                 Test set of 
                 Test data of 
                 12 speakers, 100 
               
               
                 standard common 
                 standard common 
                 commands per speaker 
               
               
                 language 
                 language 
               
               
                 Development set of 
                 Development data of 
                 20 speakers, 50 long 
               
               
                 Chuan common 
                 Chuan dialectal 
                 sentences per 
               
               
                 language 
                 common language 
                 speaker 
               
               
                 Test set of Chuan 
                 Test data of Chuan 
                 15 speakers, 75 
               
               
                 common language 
                 dialectal common 
                 commands per speaker 
               
               
                   
                 language 
               
               
                 Development set of 
                 Development data of 
                 20 speakers, 50 long 
               
               
                 Minnan common 
                 Minnan dialectal 
                 sentences per 
               
               
                 language 
                 common language 
                 speaker 
               
               
                 Test set of Minnan 
                 Test data of Minnan 
                 15 speakers, 75 
               
               
                 common language 
                 dialectal common 
                 commands per speaker 
               
               
                   
                 language 
               
               
                   
               
             
          
         
       
     
     As evident from Table 1, data are divided into the standard common language, the Chuan (an abbreviation of Sichuan Dialect) dialectal common language, and the Minnan dialectal common language, and the data are also divided into two parts, namely data for training/development and data for testing. 
     Baseline: 
     
       
         
               
             
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 (Description of a test baseline system) 
               
             
          
           
               
                   
                 Word Error Rate (WER) 
               
               
                   
                 Test set 
               
             
          
           
               
                   
                   
                   
                   
                 Test set of 
               
               
                   
                   
                 Test set of 
                 Test set of 
                 Chuan 
               
               
                   
                   
                 standard 
                 Minnan 
                 dialectal 
               
               
                   
                 Recognition 
                 common 
                 dialectal 
                 common 
               
               
                   
                 model 
                 language 
                 language 
                 language 
               
               
                   
                   
               
               
                   
                 Mixed training 
                 8.5% 
                 21.7% 
                 21.1% 
               
               
                   
                 recognition 
               
               
                   
                 model 
               
               
                   
                   
               
             
          
         
       
     
     A mixed training recognition model is used in the baseline. This mixed training recognition model is trained based on the total of three kinds of data (standard and 2 dialectal). 
     Results of experiments: 
     
       
         
               
             
               
               
             
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Results of experiments 
               
             
          
           
               
                   
                 Word Error Rate (WER) 
               
               
                   
                 Test set 
               
             
          
           
               
                   
                   
                   
                 Test set of 
               
               
                   
                 Test set of 
                 Test set of 
                 Chuan 
               
               
                   
                 standard 
                 Minnan 
                 dialectal 
               
               
                 Recognition 
                 common 
                 dialectal 
                 common 
               
               
                 model 
                 language 
                 common language 
                 language 
               
               
                   
               
               
                 Recognition 
                 6.3% 
                 11.2% 
                 15.0% 
               
               
                 model according 
               
               
                 to the present 
               
               
                 embodiment 
               
               
                   
               
             
          
         
       
     
     As evident from Table 3, the use of a model trained by employing the method of calculation according to the present embodiment obviously improves the recognition rate for two dialects as well. At the same time, the recognition rate for the standard common language is significantly improved. Thus the methods according to the above-described embodiment prove viable and effective. 
     Further, according to the above-described methods, the final recognition model can be obtained by iteratively merging each dialectal-accented common-language model into the standard common-language model.