Patent Publication Number: US-8126711-B2

Title: Method and module for modifying speech model by different speech sequence

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit of Taiwan application serial no. 96144116, filed on Nov. 21, 2007. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a speech recognition technique. More particularly, the present invention relates to a modifying method for a speech model. 
     2. Description of Related Art 
     With development of speech recognition technique, various electronic devices such as television, audio devices etc. may be operated via speeches. A user may operate these electronic devices via recognizable speech instructions of a speech recognition system. Besides applying to the electronic devices, the speech recognition technique is widely applied in related realms such as speech input, identity recognition etc. 
     Errors occurred to the speech recognition system includes substitution error, deletion error and insertion error. Referring to table 1, if the speeches of the user are respectively “A, B, C”, and a recognition result of the speech recognition system is “D, B, C”, such error is referred to as the substitution error. If the speeches of the user are respectively “A, B, C”, and the recognition result of the speech recognition system is “A, C”, such error is referred to as the deletion error. If the speeches of the user are respectively “A, B, C”, and the recognition result of the speech recognition system is “A, B, C, D”, such error is referred to as the insertion error. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Types of recognition errors 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Substitution error 
                 Correct answer 
                 A 
                 B 
                 C 
                   
               
               
                   
                 Recognition result 
                 D 
                 B 
                 C 
               
               
                 Deletion error 
                 Correct answer 
                 A 
                 B 
                 C 
               
               
                   
                 Recognition result 
                 A 
                   
                 C 
               
               
                 Insertion error 
                 Correct answer 
                 A 
                 B 
                 C 
               
               
                   
                 Recognition result 
                 A 
                 B 
                 C 
                 D 
               
               
                   
               
            
           
         
       
     
     To solve the recognition errors, a representative speech model should be applied by the speech recognition system for comparing with the speeches of the user, so as to correctly recognize the speeches of the user. To obtain the representative speech model, a speech database may provide a large amount of speeches to the speech recognition system for modifying (or training) the speech model, wherein the large amount of speeches is a collection of speeches of a plurality of people, so as to improve a maximum likelihood of the speech model. Then, a discriminative training is applied for modifying the speech model to improve a discrimination of the speech model. Since the discrimination of the speech model greatly relates to a recognition rate of the speech recognition system, if the discrimination of the speech model is improved, the recognition rate of the speech recognition system then may be improved. 
     Presently, a preferred and a commonly used modifying method for the speech model is to modify the speech model based on a sequence generated by a fixed candidate sequence generator, for example, the modifying methods disclosed by U.S. Pat. No. 5,606,644 and U.S. Pat. No. 5,579,436. However, the fixed candidate sequence generator may easily cause an uneven distribution of the error types of the generated sequences. Therefore, the speech model obtained by a conventional training method is not desirable. Detailed description will be made with reference of  FIG. 1 . 
       FIG. 1  is a curve diagram illustrating a training process of a speech model according to sequences with insertion errors generated by a conventional fixed sequence generator. Table 2 are experimental data of a speech model modified by sequences with insertion errors and generated by the conventional fixed sequence generator. Referring to  FIG. 1  and table 2, curves  101 ,  102  and  103  are respectively error rate curves of the insertion error, the substitution error and the deletion error. When modifying times of the speech model reaches  20 , the curves  101 ,  102  and  103  are convergent. According to the table 2, it is obvious that the conventional technique may effectively reduce the insertion error rate. However, the convention technique cannot reduce the substitution error rate, and even may worsen the deletion error rate. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Experimental data of a speech model modified by sequences with 
               
               
                 insertion errors and generated by the conventional fixed 
               
               
                 sequence generator. 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Insertion 
                 Deletion 
                 Distribution 
                 Digit 
                 Sentence 
               
               
                   
                 error rate 
                 error rate 
                 error rate 
                 accuracy 
                 accuracy 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Baseline 
                 4.06 
                 1.52 
                 1.64 
                 92.79 
                 74.67 
               
               
                 Conven- 
                 1.33 
                 1.70 
                 1.64 
                 95.33 
                 79.67 
               
               
                 tional 
               
               
                 technique 
               
               
                 Error 
                 67.24 
                 −11.84 
                 0.00 
                 35.23 
                 19.74 
               
               
                 reduction 
               
               
                 rate 
               
               
                   
               
            
           
         
       
     
     In summary, in the conventional technique, the signal fixed sequence generator is used for generating the sequences to modify the speech model. Since distribution of the error types of the generated sequences is uneven, only the error rate of a part of the error types is reduced, and meanwhile the error rates of the other part of the error types are worsened. 
     SUMMARY OF THE INVENTION 
     The present invention is directed to a modifying method for a speech model, so as to increase a discrimination of the speech model. 
     The present invention is directed to a modifying module for a speech model, so as to increase a recognition rate of the speech recognition. 
     The present invention provides a modifying method for a speech model. In the modifying method, a correct sequence of a speech is generated according to a correct sequence generating method and the speech model. One of a plurality of candidate sequence generating methods is selected according to a selection sequence, and a candidate sequence of the speech is generated according to the selected candidate sequence generating method and the speech model. In addition, the speech model is modified according to the correct sequence and the candidate sequence of the speech model. 
     According to another aspect of the present invention, a modifying module for a speech model is provided. The modifying module includes a speech database, a correct sequence generator, a candidate sequence module and a speech model modifier. The speech database is used for providing speeches. The correct sequence generator is coupled to the speech database and generates a correct sequence of a speech according to the speech model. The candidate sequence module is coupled to the speech database and includes a plurality of candidate sequence generators. The candidate sequence module may select a first candidate sequence generator from the plurality of candidate sequence generators according to a selection sequence, and the first candidate sequence generator may generate a candidate sequence of the speech according to the speech model. The speech model modifier is coupled to the correct sequence generator and the candidate sequence module, and modifies the speech model according to the correct sequence and candidate sequence of the speech. 
     In the present invention, the correct sequence of the speech is generated according to the correct sequence generator and the speech model. Moreover, one of the plurality of candidate sequence generating methods is selected according to the selection sequence, and the candidate sequence of the speech is generated according to the selected candidate sequence generating method and the speech model. In addition, the speech model is modified according to the correct sequence and the candidate sequence of the speech. By such means, the discrimination of the speech model is improved. 
     In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, a preferred embodiment accompanied with figures is described in detail below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a curve diagram illustrating a training process of a speech model according to sequences with insertion errors generated by a conventional fixed sequence generator. 
         FIG. 2  is a schematic diagram of a modifying module for a speech model according to an embodiment of the present invention. 
         FIG. 3  is a flowchart illustrating a modifying method for a speech model according to an embodiment of the present invention. 
         FIG. 4  is a flowchart illustrating a method for selecting a candidate sequence generator according to an embodiment of the present invention. 
         FIG. 5  is a flowchart illustrating steps of modifying a speech model according to a correct sequence and a candidate sequence of a speech. 
         FIG. 6  is a curve diagram illustrating a modification process of a speech model. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the conventional technique, a fixed sequence generator generates a sequence with a specific error type for modifying a speech model, by which only an error rate of the specific error type may be reduced, and reduction of the error rates of the other error types cannot be achieved. 
     Accordingly, in the embodiments of the present invention, candidate sequences with different error types are generated according to different candidate sequence generating methods by applying a plurality of candidate sequence generators, so as to substitute the conventional fixed candidate sequence generator. By alternately utilizing the plurality of candidate sequence generators, the speech model may be modified by candidate sequences with different error types. Therefore, the uneven distribution of the error types of the conventional candidate sequences may be mitigated to improve the modification deviation of the speech mode. 
       FIG. 2  is a schematic diagram of a modifying module for a speech model according to an embodiment of the present invention. Referring to  FIG. 2 , the modifying module  10  includes a speech database  20 , a correct sequence generator  30 , a candidate sequence module  40  and a speech model modifier  50 . The speech database  20  is used for providing speeches. The correct sequence generator  30  is coupled to the speech database  20  and may generate a correct sequence of a speech according to a speech model  60 . The candidate sequence module  40  is coupled to the speech database  20  and includes a plurality of candidate sequence generators. 
     The candidate sequence module  40  may select a candidate sequence generator from the plurality of candidate sequence generators according to a selection sequence SS, and the selected candidate sequence generator may generate a candidate sequence of the speech according to the speech model  60 . The speech model modifier  50  is coupled to the correct sequence generator  30  and the candidate sequence module  40 , and modifies the speech model  60  according to the correct sequence and candidate sequence of the speech. 
     In the present embodiment, the plurality of candidate sequence generators is respectively represented by candidate sequence generators  41 ,  42  and  43 . The candidate sequence generator  41  applies a candidate sequence generating method liable to generate a candidate sequence with an insertion error, and therefore the candidate sequence generator  41  is liable to generate the candidate sequence with the insertion error. The candidate sequence generator  42  applies a candidate sequence generating method liable to generate a candidate sequence with a substitution error, and therefore the candidate sequence generator  42  is liable to generate the candidate sequence with the substitution error. The candidate sequence generator  43  applies a candidate sequence generating method liable to generate a candidate sequence with a deletion error, and therefore the candidate sequence generator  43  is liable to generate the candidate sequence with the deletion error. The candidate sequence generators  41 ,  42  and  43  and the candidate sequence generating methods thereof will be described in detail below. 
     Generally, a speech recognition device may recognize the speech via two recognition networks respectively with a fixed length and a non-fixed length. Taking recognition of a Chinese numeric string as an example, staff numbers or document numbers generally have the fixed lengths, and therefore the recognition network with the fixed length may be applied for recognizing. If a content to be recognized has the non-fixed length, the recognition network with the non-fixed length may be applied for recognizing. In the present embodiment, speeches within the speech database  20  are all known, and accordingly lengths of the speeches are known. Therefore, based on such information, candidate sequence generators with different candidate sequence generating methods may be designed according to the two recognition networks with the fixed length and the non-fixed length. 
     To be specific, the candidate sequence generator  41  applies a speech recognition device liable to generate the insertion errors, which may perform recognition based on the recognition network with the non-fixed length, and selects the candidate sequence with a length being greater than that of a correct answer, so as to generate relatively more insertion errors. The candidate sequence generator  42  applies a speech recognition device liable to generate the substitution errors, which may perform recognition based on the recognition network with the fixed length to obtain the candidate sequence with the length equals to that of the correct answer, so as to generate relatively more substitution errors. The candidate sequence generator  43  applies a speech recognition device liable to generate the deletion errors, which may perform recognition based on the recognition network with the non-fixed length, and selects the candidate sequence with a length being less than that of a correct answer, so as to generate relatively more deletion errors. 
     On the other hand, the selection sequence SS may be an alternate arrangement of the candidate sequence generators  41 ,  42  and  43 , as shown in table 3. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Selection sequence 
               
            
           
           
               
               
            
               
                   
                 Candidate sequence generators 
               
               
                 Sequence 
                 selected by the candidate sequence 
               
               
                 number 
                 module 40 
               
               
                   
               
               
                 1 
                 Candidate sequence generator 41 
               
               
                 2 
                 Candidate sequence generator 42 
               
               
                 3 
                 Candidate sequence generator 41 
               
               
                 4 
                 Candidate sequence generator 42 
               
               
                 5 
                 Candidate sequence generator 43 
               
               
                   
               
            
           
         
       
     
       FIG. 3  is a flowchart illustrating a modifying method for a speech model according to an embodiment of the present invention. Referring to  FIG. 2  and  FIG. 3 , a first speech is provided by the speech database  20  for the speech model modifier  50  to perform a first modification to the speech model  60 . First, in step S 301 , the correct sequence generator  30  generates a correct sequence of the first speech according to the correct sequence generating method and the speech model  60 . Next, in step S 302 , the candidate sequence module  40  selects a candidate sequence generator from the plurality of candidate sequence generators according to the selection sequence SS. According to the table 3, the candidate sequence module  40  of the present embodiment may select the candidate sequence generator  41  with a sequence number of “1”. Next, in step S 303 , the candidate sequence generator  41  generates the candidate sequence of the first speech based on the candidate sequence generating method liable to generate the insertion errors. Next, in step S 304 , the speech model modifier  50  modifies the speech model  60  according to the correct sequence and the candidate sequence of the first speech. 
     When a second speech is provided by the speech database  20  for the speech model modifier  50  to perform a second modification to the speech model  60 , the correct sequence generator  30  may provide the correct sequence of the second speech to the speech model modifier  50  (step S 301 ). The candidate sequence module  40  then selects the candidate sequence generator  42  with the sequence number of “2” according to the selection sequence SS (step S 302 ), so as to generate the candidate sequence of the second speech for the speech model modifier  50  (step S 303 ). Next, the speech model modifier  50  modifies the speech model  60  according to the correct sequence and the candidate sequence of the second speech (step S 304 ). 
     Deduced by analogy, when the speech database  20  provides a third speech for the speech model modifier  50  to perform a third modification to the speech model  60 , the candidate sequence module  40  then selects the candidate sequence generator  41  with the sequence number of “3” according to the selection sequence SS. When the speech database  20  provides a fourth speech for the speech model modifier  50  to perform a fourth modification to the speech model  60 , the candidate sequence module  40  then selects the candidate sequence generator  42  with the sequence number of “4” according to the selection sequence SS. 
     When the speech database  20  provides a fifth speech for the speech model modifier  50  to perform a fifth modification to the speech model  60 , the candidate sequence module  40  then selects the candidate sequence generator  43  with the sequence number of “5” according to the selection sequence SS. When the speech database  20  provides a sixth speech for the speech model modifier  50  to perform a sixth modification to the speech model  60 , the candidate sequence module  40  then again selects the candidate sequence generator  41  with the sequence number of “1” according to the selection sequence SS. An embodiment is provided below to explain how the candidate sequence module  40  selects one of the plurality of candidate sequence generators (step S 302 ). 
     First, a counter (not shown) may be set to the candidate sequence module  40 , this counter may count a number of times that the candidate sequence  40  selects the candidate sequence generator. Assuming an initial counting value of the counter is “1”, each time the candidate sequence module  40  selects one of the plurality of candidate sequence generators, the counting value adds 1. By such means, the candidate sequence module  40  may implement the selection sequence SS shown in the table 3 according to the counting value. 
     For example,  FIG. 4  is a flowchart illustrating a method for selecting a candidate sequence generator according to an embodiment of the present invention. Referring to  FIG. 2  and  FIG. 4 , first, in step S 401 , a control parameter is set by the candidate sequence module  40 , and in the present embodiment, the control parameter is set to 5. Next, in step S 402 , the counting value is determined whether or not to be equal to the control parameter, and if the counting value is not equal to the control parameter, step S 403  is then executed. Conversely, if the counting value is equal to the control parameter, step  407  is then executed. During the first modification of the speech model  60 , the initial counting value “1” is not equal to the control parameter “5”, and therefore the step S 403  is executed, in which the counting value is determined whether or not to be an odd number, and if the counting value is an odd number, step S 404  is then executed; conversely, if the counting value is not an odd number, step S 405  is then executed. Since the counting value “1” is the odd number, the step S 404  is executed, by which the candidate sequence generator  41  is selected. Next, the counting value adds 1 (step S 406 ) and the step S 402  is then repeated. 
     Accordingly, during the second modification of the speech model  60 , the counting value is “2”, and then the steps S 402 , S 403  and S 405  are sequentially executed to select the candidate sequence generator  42 . Next, the counting value adds 1 (step S 406 ), and the step S 402  is then repeated. During the third modification of the speech model  60 , the counting value is “3”, and then the steps S 402 , S 403  and S 404  are sequentially executed to select the candidate sequence generator  41 . Next, the counting value adds 1 (step S 406 ), and the step S 402  is then repeated. During the fourth modification of the speech model  60 , the counting value is “4”, and then the steps S 402 , S 403  and S 404  are sequentially executed to select the candidate sequence generator  42 . Next, the counting value adds 1 (step S 406 ), and the step S 402  is then repeated. 
     Accordingly, during the fifth modification of the speech model  60 , the counting value is “5”, and then the steps S 402  and S 407  are sequentially executed to select the candidate sequence generator  43 . Next, the counting value is set to “1” again (step S 408 ), and the step S 402  is then repeated. During the sixth modification of the speech model  60 , the counting value is “1”, and then the steps S 402 , S 403  and S 404  are sequentially executed to select the candidate sequence generator  41 . Next, the counting value adds 1 (step S 406 ), and the step S 402  is then repeated. Deduced by analogy, and the repeated description is omitted hereby. Next, the step S 304  is further described in detail. 
       FIG. 5  is a flowchart illustrating steps of modifying a speech model according to a correct sequence and a candidate sequence of a speech. Referring to  FIG. 2  and  FIG. 5 , first, in step S 501 , a misrecognition measure is calculated by the speech model modifier  50  according to the correct sequence, the candidate sequence and the speech model  60 . Next, in step S 502 , the misrecognition measure is input to a loss function. Next, the speech model modifier  50  modifies the speech model  60  with a fine-tuning approach to minimize the loss function, so as to improve the discrimination of the speech model  60 . 
     To further explain the advantages of the embodiment of the present invention, a part of experimental data is provided to those skilled in the art for reference. In the present embodiment, experimental result for recognition of the Chinese numeral string is taken as an example. Training corpus of the Chinese numeral database includes 5080 sentences, and test corpus includes four groups, which respectively are MAT_TS, ITRI_ID, NTUT_CARD and NTUT_CONFUSION, wherein the MAT_TS has 757 sentences, the ITRI_ID has 1243 sentences, the NTUT_CARD has 475 sentences and the NTUT_CONFUSION has 3252 sentences. The speech model  60  applies 19 context independent phone models, and each model has 3 states, and 16 mixture per state. Moreover, a silence model including 3 states and a short silence model with one state are further trained. 
       FIG. 6  is a curve diagram illustrating a modification process of a speech model. Table 4 is a comparison table between experimental data of the present embodiment and that of the convention technique. Referring to  FIG. 6  and table 4, curves  111 ,  112  and  113  are respectively error rate curves of the insertion error, the substitution error and the deletion error. When modification times of the speech model  60  reaches  62 , the curves  111 ,  112  and  113  are convergent. Comparing to  FIG. 1 , it is obvious that the curve  111  of the insertion error has an obvious oscillation phenomenon, this is due to alternate utilization of the candidate sequences with different error features, such that different errors may be alternately modified, and therefore difference among occurrence rates of the three error types is obviously reduced. 
     Referring to table 4, it is obvious that for different test corpuses, the method of the present invention may not only greatly reduce the insertion error, the deletion error and the substitution error, but may also greatly improve the speech recognition rate. Comparing to a baseline system, sentence error rate of the conventional technique may be reduced 20%˜36.6%, and digit error rate may be reduced 16.96%˜39.85% according to the conventional technique, while according to the method of the present invention, the sentence error rate may be reduced 27.69%˜41.74%, and the digit error rate may be reduced 30.94%˜49.81%. Therefore, the method of the present invention may further reduce the error rate compared to the conventional technique. 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 A comparison table between the experimental data of the present 
               
               
                 method and the conventional method 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                   
                   
                   
                 Sentence 
               
               
                   
                   
                   
                   
                 Substitution 
                   
                   
                 Digit error 
                 error 
               
               
                 Test 
                   
                 Insertion 
                 Deletion 
                 error 
                 Digit 
                 Sentence 
                 reduction 
                 reduction 
               
               
                 corpus 
                 Model 
                 error rate 
                 error rate 
                 rate 
                 accuracy 
                 accuracy 
                 rate 
                 rate 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 MAT_TS 
                 Baseline 
                 1.73 
                 1.70 
                 1.89 
                 94.68 
                 76.88 
                   
                   
               
               
                   
                 Conventional 
                 0.41 
                 1.70 
                 1.08 
                 96.80 
                 85.34 
                 39.85 
                 36.60 
               
               
                   
                 technique 
               
               
                   
                 Present 
                 0.53 
                 1.29 
                 0.85 
                 97.33 
                 86.53 
                 49.81 
                 41.74 
               
               
                   
                 embodiment 
               
               
                 ITRI_ID 
                 Baseline 
                 1.19 
                 2.55 
                 1.98 
                 94.27 
                 72.73 
               
               
                   
                 Conventional 
                 0.17 
                 2.40 
                 1.60 
                 95.83 
                 81.17 
                 27.23 
                 30.95 
               
               
                   
                 technique 
               
               
                   
                 Present 
                 0.25 
                 1.96 
                 1.45 
                 96.34 
                 81.74 
                 36.13 
                 33.04 
               
               
                   
                 embodiment 
               
               
                 NTUT_CARD 
                 Baseline 
                 1.76 
                 1.74 
                 2.22 
                 94.28 
                 45.26 
               
               
                   
                 Conventional 
                 0.61 
                 2.10 
                 2.05 
                 95.25 
                 56.21 
                 16.96 
                 20.00 
               
               
                   
                 technique 
               
               
                   
                 Present 
                 0.74 
                 1.48 
                 1.72 
                 96.05 
                 60.42 
                 30.94 
                 27.69 
               
               
                   
                 embodiment 
               
               
                 NTUT_CONFUSION 
                 Baseline 
                 4.15 
                 3.52 
                 3.50 
                 88.84 
                 65.20 
               
               
                   
                 Conventional 
                 1.28 
                 2.78 
                 3.23 
                 92.72 
                 74.79 
                 34.77 
                 27.56 
               
               
                   
                 technique 
               
               
                   
                 Present 
                 2.07 
                 2.33 
                 2.63 
                 92.98 
                 75.99 
                 37.10 
                 31.01 
               
               
                   
                 embodiment 
               
               
                   
               
            
           
         
       
     
     In summary, in the present embodiment, the candidate sequence module  40  selects the candidate sequence generator according to the selection sequence SS, so as to generate the candidate sequence with different error types. By such means, modification deviation of the speech model caused by applying the candidate sequence with the fixed error type provided by the conventional method may be mitigated. Therefore, the discrimination of the speech model  60  may be greatly improved, such that the recognition rate of the speech recognition is improved. 
     It should be noted that though a preferred modifying method for the speech model and a modifying module thereof are provided in the aforementioned embodiment, those skilled in the art should be understood that different manufacturers may have different designs of the modifying method and the modifying module for the speech model, and therefore the present invention is not limited to the preferred modifying method and the modifying module provided above. In other words, as long as the modifying method and the modifying module for the speech model may generate the candidate sequence with different error types according to the selection sequence, it is considered to be within the spirit and scope of the present invention. Now, the aforementioned steps will now be described more fully for fully conveying the concept of the invention to those skilled in the art. 
     Referring to  FIG. 2  again, in the aforementioned embodiment, the selection sequence SS provided by the table 3 is only for a selection example, and the present invention is not limited thereto. In other words, those skilled in the art may set the selection sequence SS according to an actual requirement, so as to provide the candidate sequences with different error types. For example, the table 3 may be substitute by a table 5. Moreover, the selection sequence SS may also be an alternate arrangement of the candidate sequence generators  41  and  42 , shown as table 6. 
     
       
         
           
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                 Another selection sequence table 
               
            
           
           
               
               
            
               
                   
                 Candidate sequence generator selected 
               
               
                 Sequence 
                 by the candidate sequence 
               
               
                 number 
                 module 40 
               
               
                   
               
               
                 1 
                 Candidate sequence generator 42 
               
               
                 2 
                 Candidate sequence generator 41 
               
               
                 3 
                 Candidate sequence generator 42 
               
               
                 4 
                 Candidate sequence generator 41 
               
               
                 5 
                 Candidate sequence generator 43 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                 Still another selection sequence table 
               
            
           
           
               
               
            
               
                   
                 Candidate sequence generator selected 
               
               
                 Sequence 
                 by the candidate sequence 
               
               
                 number 
                 module 40 
               
               
                   
               
               
                 1 
                 Candidate sequence generator 41 
               
               
                 2 
                 Candidate sequence generator 42 
               
               
                   
               
            
           
         
       
     
     Moreover, in  FIG. 2 , though only the candidate sequence generators  41 ˜ 43  are provided for representing the plurality of candidate sequence generators of the candidate sequence module  40 . However, in another embodiment, number of the candidate sequence generators may be varied, and even the candidate sequence generators of other types may be applied. 
     On the other hand, in the embodiment of  FIG. 4 , the control parameter is “5”, however, the present invention is not limited thereto. Those skilled in the art may set the control parameter according to the selection sequence by themselves, or even change the steps of  FIG. 4 . 
     In summary, according to the modifying method for the speech model of the present invention, different candidate sequence generating methods may be selected according to the selection sequence, so as to generate the candidate sequences with different error types. Therefore, modification deviation of the speech model caused by applying the candidate sequence with the fixed error type provided by the conventional method may be mitigated. Therefore, the discrimination of the speech model may be greatly improved and the recognition rate of the speech recognition is improved. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the present invention without departing from the scope or spirit of the invention. In view of the foregoing, it is intended that the present invention cover modifications and variations of this invention provided they fall within the scope of the following claims and their equivalents.