Abstract:
A method of constructing a model of recognizing English pronunciation variations is used to recognize English pronunciations with different intonations influenced by native languages. The method includes collecting a plurality of sound information corresponding to English expressions; corresponding phonetic alphabets of the native language and English of a region to International Phonetic Alphabets (IPAs), so as to form a plurality of pronunciation models; converting the sound information with the pronunciation models to form a pronunciation variation network of the corresponding English expressions, thereby detecting whether the English expressions have pronunciation variation paths; and finally summarizing the pronunciation variation paths to form a plurality of pronunciation variation rules. Furthermore, the pronunciation variations are represented by phonetics features to infer possible pronunciation variation rules, which are stored to form pronunciation variation models. The construction of the pronunciation variation models enhances applicability of an English recognition system and accuracy of voice recognition.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of Invention 
         [0002]    The present invention relates to a method of constructing a model of recognizing English pronunciations, and more particularly to a method of constructing a model of recognizing English pronunciation variations. 
         [0003]    2 Related Art 
         [0004]    The first language of each country is a kind of common language among all ethnic groups, which is the one selected from the languages of the ethnic groups or regions in this country, so as to facilitate communication among the ethnic groups in this country. It is also feasible among countries. 
         [0005]    Currently, English is the popular universal language, and in order to enable the public to know its pronunciations, the corresponding phonetic alphabets are used, such as KK phonetic alphabet (created by John Samuel Kenyon and Thomas A. Knott in the United States), DJ phonetic alphabet (created by Daniel Jones in U.K.), or the International Phonetic Alphabet (IPA) which are popular all over the world. However, living products are gradually computerized currently, and a voice recognition model is usually adopted to activate a product. Therefore, people pay more attention to the voice recognition technology. 
         [0006]    In order to achieve the voice recognition technology, pronunciations of the spoken English expressions (sentences, phrases, words, and letters) by using the IPA are recorded and then collected, and finally compiled into a corpus. A pronunciation lexicon, such as a CMU pronunciation lexicon compiled by the Carnegie Mellon University (CMU) and containing about 120,000 expressions, records English expressions and the corresponding IPAs, in which each phonetic alphabet corresponds to a sound characteristic value. 
         [0007]    When any English voice recognition system utilizes the CMU pronunciation lexicon, the system converts the pronunciation of an English expression into a corresponding sound characteristic value, and compares this sound characteristic value with the sound characteristic value recorded in the CMU pronunciation lexicon, so as to obtain the corresponding English expression. 
         [0008]    However, the prior art has the unavoidable defects. 
         [0009]    Firstly, when the native language of a speaker is not English, i.e., the speaker is not from a British/American English speaking country, his/her English pronunciations are mostly influenced by intonations or pronunciation habits of the native language. For example,  FIGS. 1A to 1C  show incorrect English pronunciations of Taiwanese under the influence of mandarin, i.e., the pronunciation variations cannot be found in the IPAs. However, the current voice recognition system usually adopts the pronunciation lexicon formed of standard American/British English samples. Therefore, if the parsed sound characteristic value cannot be found in the pronunciation lexicon, the correct English expressions cannot be parsed correctly. 
         [0010]    Secondary, the conventional voice recognition technology predefines all possible pronunciations (including true pronunciations and assumptive pronunciations), and only the pronunciation variations appearing in the corpus are defined in the pronunciation lexicon, for example, for the English letter A, the phonetic alphabet thereof and the sound characteristic values of the possible pronunciation variations are collected. The pronunciations not included in the corpus and pronunciations in a non-English speaking region such as fifty Japanese phonetic alphabets, thirty-seven Chinese phonetic alphabets will not be defined, so the range of the pronunciations that can be parsed is too narrow. 
       SUMMARY OF THE INVENTION 
       [0011]    In view of the above, the present invention is directed to provide a method of constructing a pronunciation variation model according to the native language of a region and the English phonetic alphabets, which may recognize English pronunciation variations of the public in that region under the influence of the native language. 
         [0012]    In order to achieve the aforementioned object, the present invention provides a method of constructing a model of recognizing English pronunciation variations, which is used for recognizing English pronunciations with intonations caused by different native languages. The construction method includes firstly providing a plurality English expressions and corresponding phonetic alphabets and collecting a plurality of sound information corresponding to the English expressions; corresponding the phonetic alphabets of the native language and English of a region to a plurality of International Phonetic Alphabets (IPAs), so as to form a plurality of pronunciation models; converting the sound information of the English expressions by using the pronunciation models, so as to form a pronunciation variation network of the corresponding English expression, thereby detecting whether the English expressions have pronunciation variation paths; and finally summarizing the pronunciation variation paths to form a plurality of pronunciation variation rules and storing the rules to form a pronunciation variation model. 
         [0013]    The method provided by the present invention may also assume in a form of a recording medium, and may be executed by reading a computer program stored in the recording medium, thereby solving the same problem through the same method and achieving the same efficacies. 
         [0014]    The present invention achieves the efficacies that the prior art cannot achieve, and constructs a pronunciation variation model for each region, so as to enable the current English recognition system to recognize English pronunciations of the public in a region with intonations caused by a different native language through the pronunciation variation model. Or, when researchers in each region intend to construct an exclusive English recognition system for this region, they may construct such a system according to the method of constructing the pronunciation variation model. No matter whether the pronunciation variation model is created in the former or latter English recognition system, the accuracy of recognizing voice may be enhanced, thereby enhancing the applicability of the English recognition system. 
         [0015]    Further scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0016]    The present invention will become more fully understood from the detailed description given herein below for illustration only, and thus are not limitative of the present invention, and wherein: 
           [0017]      FIGS. 1A to 1C  show corresponding tables of common incorrect pronunciations of phonetic alphabets; 
           [0018]      FIG. 2  is a flow chart of processes of constructing a pronunciation variation model according to an embodiment of the present invention; 
           [0019]      FIG. 3  is a partial schematic view of the CMU pronunciation lexicon according to an embodiment of the present invention; 
           [0020]      FIGS. 4A to 4F  are schematic views of grouping the sound characteristic values of the pronunciation model; 
           [0021]      FIG. 5  is a creation view of the pronunciation variation network according to one embodiment of the present invention; 
           [0022]      FIG. 6  is a table of phonetic alphabets of the IPA corresponding to the pronunciation types according to one embodiment of the present invention; 
           [0023]      FIG. 7  is a schematic view of a phone confusion matrix according to one embodiment of the present invention; 
           [0024]      FIG. 8  is a referencing table of the arrangement of the phonetic alphabets according to one embodiment of the present invention; 
           [0025]      FIG. 9  is a reference schematic view of the characterization of the phonetic alphabets of the sounds according to one embodiment of the present invention; 
           [0026]      FIG. 10  is a schematic view of pronunciation variations according to one embodiment of the present invention; 
           [0027]      FIG. 11  is a reference schematic view of the characteristics of the sounds according to one embodiment of the present invention; and 
           [0028]      FIG. 12  is a schematic view of the path of pronunciation variations according to one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0029]    In order to make the object, structure, features, and functions of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below. 
         [0030]    Referring to  FIG. 2 , a flow chart of processes of constructing a pronunciation variation model according to an embodiment of the present invention is shown. The pronunciation variation model is used to recognize English pronunciations with different intonations influenced by native language, and the construction method comprises the following steps. 
         [0031]    Provide a plurality of English expressions and at least one phonetic alphabet corresponding to each of the English expressions, and collect a plurality of corresponding sound information according to the phonetic alphabet of the English expression (Step S 210 ). This step is illustrated based on the CMU pronunciation lexicon compiled by the Carnegie Mellon University (CMU). 
         [0032]    Referring to  FIG. 3 , a schematic partial view of the CMU pronunciation lexicon according to an embodiment of the present invention is shown. Each of the English expressions in the pronunciation lexicon has its correct phonetic alphabet, and is arranged in the order of sorting number, English expression, and phonetic alphabet. 
         [0033]    The phonetic alphabets in this embodiment are based on the IPA, and the collected sound information is provided by the public in the same region, ethnic group or country in which the native language is not English. In the following, the English pronunciations of Taiwanese are taken as the samples of the sound information. 
         [0034]    Correspond the phonetic alphabets of the native language and English to a plurality of IPAs, so as to form a plurality of pronunciation models (Step S 220 ). For example, thirty-seven pronunciations of the Chinese phonetic symbols in Taiwan and thirty-nine pronunciations of the English phonetic alphabets are correspondingly formed into fifty-five IPAs. 
         [0035]    Collect a plurality of phonetic alphabet pronunciations directed to one of the IPAs, and convert each of the phonetic alphabet pronunciations into a corresponding characteristic value. As shown in  FIGS. 4A to 4F , for example, as for the English expression b, firstly collect a plurality of Taiwanese&#39;s pronunciations of the phonetic alphabet pronunciations of b and transform the phonetic alphabet pronunciations of English expression b into relevant characteristic values  401  by using a Fourier Transform equation. Then, form the characteristic values  401  into a value group  410  and calculate a grouping threshold value corresponding to the characteristic values  401 . The grouping threshold value is not an absolute threshold, but an optimal corresponding value calculated by using a statistical method according to the quantity of characteristic values  401 . 
         [0036]    Then, calculate a mean value  402  of the value group  410 , in which all characteristic values  401  are summarized firstly, thereby getting the mean value  402 . Next, calculate numerical distances between the mean value  402  and each of the characteristic values  401 , so as to obtain a first characteristic value  403  from the value group  410  which is away from the mean value  402  by a maximum numerical distance. Afterwards, calculate a second characteristic value  404  in the same value group  410  which is away from the first characteristic value  403  by a maximum numerical distance. 
         [0037]    Then, calculate the numerical distances between each of the characteristic values  401  and the first characteristic value  403  and between each of the characteristic values  401  and the second characteristic value  404 , and adopt a small result from the calculation results to determine whether each of the characteristic values  401  corresponds to the first characteristic value  403  or the second characteristic value  404 , thereby forming a value group  410  containing the characteristic values  401  close to the first characteristic value  403  and a value group  420  containing the characteristic values  401  close to the second characteristic value  404 , respectively. Subsequently, obtain a within-group distance  431  and a between-group distance  432  of the two value groups, thereby calculating a grouping standard. 
         [0038]    The so-called between-group distance  432  refers to a distance between any value group and other value groups, and is a distance between the mean values of each value group. The within-group distance  431  refers to the summation of the distances between each of the characteristic values  401  and the mean value  402  in the same group. The grouping standard is that the between-group distance  432  divided by the within-group distance  431 . 
         [0039]    Determine whether the grouping standard is higher than the grouping threshold value through comparison, if no, obtain the value group in the pronunciation model, and if yes, continue to calculate a mean value  402  of each value group, so as to perform grouping operation, till the grouping standard is lower than the grouping threshold value. Thereby, at least one value group of the pronunciation model of the corresponding b may be obtained. The characteristic values in the value group correspond to the phonetic alphabets of the native language, i.e., correspond to the characteristic values of the phonetic symbols. Or, the value group of the characteristic values of the corresponding English phonetic alphabets is obtained. In a similar way, the pronunciation models generated by all of the phonetic symbols and the English phonetic alphabets corresponding to the IPAs may be constructed. 
         [0040]    Convert all the sound information of each of the English expressions by using the pronunciation models, and construct a pronunciation variation network corresponding to the English expression with reference to the phonetic alphabets of the English expression, so as to detect whether the English expression has a pronunciation variation path (Step S 230 ). 
         [0041]    As shown in  FIG. 5 , for example, the corresponding phonetic alphabets of the English expression “attend” are “AH, T, EH, N, D” of IPA in the CMU pronunciation lexicon in sequence, and the phonetic alphabets of the English expression are set as a reference, so as to detect whether an insertion pronunciation variation exits in each pronunciation of the phonetic alphabets, i.e., detect whether an insertion pronunciation variation exists in the pronunciation between the input of the pronunciation and “AH,” “AH” and “T.” “T” and “EH,” “EH” and “N.” “N” and “D,” “D” and the end of pronunciation by using the constructed pronunciation models. 
         [0042]    Next, detect whether a deletion pronunciation variation exits between each of the phonetic alphabet and the next phonetic alphabet. However, during the detection process, not only whether the deletion pronunciation variation exits between the two adjacent phonetic alphabets is detected, but also whether a deletion pronunciation variation exits between a phonetic alphabet and a following insertion pronunciation variation if the phonetic alphabet is followed by an insertion pronunciation variation. 
         [0043]    Finally, detect a substitution pronunciation variation corresponding to each phonetic alphabet and construct the pronunciation variation network (Step S 240 ). However, in order to reduce the complexity of the pronunciation variation network, the following two methods may be used to remove impossible pronunciation variation paths. 
         [0044]    The first method is to obtain a pronunciation type of each of the phonetic alphabets in the IPA, and use at least one IPA of the same pronunciation type as a substitution of the phonetic alphabet. As shown in  FIG. 6 , it is a table of phonetic alphabets of the IPA corresponding to the pronunciation types, and the table is divided into “Voiced plosive,” “Unvoiced plosive,” “Fricatives,” “Affricatives,” “Nasals,” “Liquids,” “Front vowels,” “Central vowels,” “Back rounded vowels,” and “Back unrounded vowels.” 
         [0045]    Compare the phonetic alphabets “AH, T, EH, N, D” of the word “attend” with the table to obtain the IPAs of the same pronunciation type. For example, as for the phonetic alphabet “T,” the pronunciation type is “Unvoiced plosive,” and only the phonetic alphabets “P” and “K” have the same pronunciation type. Therefore, the substitution pronunciation variation of the phonetic alphabet “T” merely includes “P” and “K” and it is impossible for the “T” to be replaced by other phonetic alphabets with different pronunciation types, for example, it is impossible to pronounce the phonetic alphabet “T” as “A” by mistake. Therefore, the phonetic alphabets with different pronunciation types will not be taken into account. 
         [0046]    The second method is to establish a phone confusion matrix, as shown in  FIG. 7 . That is, firstly collect all the pronunciations of the IPAs, and calculate the pronunciation probability for each IPA to be pronounced as other IPAs by mistake, so as to establish the phone confusion matrix. Then, based on the phonetic alphabets of the English expressions, take at least one IPA in a pronunciation probability range, and set the selected IPA as the substitution pronunciation variation of the phonetic alphabet. The pronunciation probability corresponding to English expression in the phone confusion matrix is as follows, (A)=0%-10%, (B)=10%-15%, (C)=15%-20%, (D)=20%-25%, (E)=25%-30%, (F)=30%-35%, (G)=35%-40%, (H)=40%-45%, (I)=45%-50%, (J)=50%-55%, (K)=55%-60%, (L)=60%-65%, (M)=65%-70%, (N)=70%-75%, (O)=75%-80%, (P)=80%-85%, (Q)=85%-90%, (R)=90%-95%, (S)=95%-100%, (T)=100%. 
         [0047]    However, in order to obtain a substitution pronunciation variation accurately while reducing the complexity of the pronunciation variation network, if the pronunciation probability is too high, for example, 100% (T), the pronunciation must be incorrect; and if the pronunciation probability is too low, for example, 0%-10%(A), it is mostly impossible to be pronounced by mistake. Therefore, the aforementioned two circumstances will not be considered to be the substitution pronunciation variation of the phonetic alphabets. 
         [0048]    For example, as for the phonetic alphabet “EH” of the English expression “attend,” compare it with the phone confusion matrix, the pronunciation probability of pronouncing “EH” correctly is 55%-60% (K), the pronunciation probability of pronouncing it as “er M” is 10%-15% (B), the pronunciation probability of pronouncing it as “AE” is 15%-20% (C), and the pronunciation probability of pronouncing it as other phonetic alphabets is 0%-5% (A). Therefore, only the phonetic alphabets “er_M” and “AE” server as the substitution pronunciation variations of the phonetic alphabets “EH,” and others will not be taken into account, thereby reducing the complexity of the pronunciation variation network of the English expression “attend” and enhancing the recognition accuracy of the pronunciation variation network. 
         [0049]    However, all the pronunciation variations (including insertion pronunciation variations, deletion pronunciation variations, and substitution pronunciation variations) are inferred by three continuous pronunciations, which should all be possible pronunciations. As shown in  FIG. 8 , for example, the phonetic alphabets of each of the expressions in the CMU pronunciation lexicon are arranged by using three continuous phonetic alphabets as a set, so as to count the times for arranging each set of phonetic alphabets in the CMU pronunciation lexicon and calculate the probability. In this manner, more than 20,000 sets of phonetic alphabets may be obtained from the CMU pronunciation lexicon, and each set of phonetic alphabets is provided with corresponding statistic times and probability, i.e., represents the circumstance of most possibly forming the insertion pronunciation variations, and the more than 20,000 sets of phonetic alphabets are arranged into a reference table of arranging the phonetic alphabets. 
         [0050]    For example, the phonetic alphabets of the English expression “attend” are “AH, T, EH, N, D,” and the times and probability of the arrangement by using the phonetic alphabets “AH, T, EH,” and “T, EH, N,” and “EH, N, D” may be found from the CMU pronunciation lexicon. In a similar way, summarize all the arrangements of the phonetic alphabets in the CMU pronunciation lexicon and the statistic probability and times. 
         [0051]    The reference table of the arrangement of the phonetic alphabets in the statistical result is shown in  FIG. 8 , which shows a part of the reference table. The arrangement of the phonetic alphabets “t_M-i_M-sil” numbered with 26 (the “sil” in the whole text and drawings are not pronounced) has the statistic probability of 5974 times, while the arrangement of the phonetic alphabets “n_M-t M-i_M” numbered with 25 has the statistic probability of 2012 times. That is, the arrangement of the phonetic alphabets “t_M-i_M-sil” numbered with 26 may be possibly read in the 120,000 English expressions in the CMU pronunciation lexicon, or the insertion pronunciation variations are formed, while it is less possible to read the arrangement of the phonetic alphabets “n_M-t_M-i_M” numbered with 25. 
         [0052]    When people not from British/American English speaking countries, such as Taiwanese, recognize the English expressions they are speaking, the reference table may be used to infer the possibility of pronouncing the English expressions in the Taiwanese&#39; habits, i.e., obtain the relatively accurate pronunciation variation network with a low complexity. 
         [0053]    Furthermore, in order to obtain the pronunciation variation rules besides the normal pronunciation variations, the English expressions may be analyzed to obtain an inference rule according to the pronunciation variation network (Step S 250 ). Firstly, correspond all the phonetic alphabets to the pronunciation characteristics of the linguistics, then, analyze the pronunciation variation network of the English expression to obtain a corresponding inference rule, and determine whether the phonetic alphabets having the same pronunciation characteristic have the same inference rule. 
         [0054]    As shown in  FIG. 9 , it is a schematic view of corresponding the phonetic alphabets to the pronunciation characteristics of the linguistics in the present invention. 
         [0055]    As shown in  FIG. 10 , as for the phonetic alphabets “AH, T, EH, N, D” in “attend,” the path is “start-AH-T-EH-N-D-sil.” Firstly, find the pronunciation variation network of “attend,” and use a data mining method to find all pronunciation variations of “attend” in the pronunciation variation network. In this example, “N-D-sil” has three pronunciation variations. 
         [0056]    Firstly, the accent of the pronunciation is strong, pronounce “N-D-sil” as “N-D-ER-sil” or “N-D-AH-sil,” i.e., form the circumstance of the insertion pronunciation variation. 
         [0057]    Secondary, the pronunciation is partially omitted, pronounce “N-D-sil” as “N-sil,” i.e., form the circumstance of the deletion pronunciation variations. 
         [0058]    Thirdly, the pronunciation is incorrect, pronounce “N-D-sil” as “N-T-sil,” i.e., form the circumstance of the substitution pronunciation variation. 
         [0059]    As such, when there is phonetic alphabet “N” before the phonetic alphabets “D,” and “sil” following it, three vocalization variations corresponding to the three pronunciation variation rules exit. 
         [0060]    Compare the phonetic alphabets “D” with the schematic view of the pronunciation characteristics of the linguistics in  FIG. 9 , thereby leading to the result in  FIG. 11 , i.e., the phonetic alphabet “D,” the phonetic alphabets “B,” and the phonetic alphabet “G” are alveolar, bilabial, and velar respectively in aspect of pronunciation positions, the pronunciation methods are all plosive and sonant. Therefore, it is determined that the pronunciation variation rule of “N-D-sil” is applicable to the phonetic alphabet “B” and the phonetic alphabet “G,” i.e., uncollected pronunciation variation rules may be inferred. 
         [0061]    Then, the data mining method is used to calculate confidence scores of each of the pronunciation variation rules to obtain the relative weight relation of each of the pronunciation variation rules and determine a precedence sequence of the pronunciation variation rules of each of the English expressions, thereby obtaining a most accurate pronunciation variation path. 
         [0062]    As shown in  FIG. 12 , the pronunciation variation path “attend” is shown. The aforementioned method is used to form the pronunciation variation network of “attend,” and then the found or inferred pronunciation variation rules are used to obtain the most simple pronunciation variation path. From the figure, it can be known that phonetic alphabet “AH” has the probability of 72% to be pronounced correctly and the probability of 28% to be pronounced as “UH” by mistake. The probability of pronouncing “D” after the phonetic alphabet “N” is 60%, and the probability of not pronouncing “sil” is 40%. The probability of not pronouncing “sil” after the phonetic alphabets “D” is 87%, and the probability of producing mixture to pronounce “AH” is 13%. The pronunciation variation path of “attend” is the optimal variation path generated after the pronunciation variation network of “attend” is simplified as far as possible by using the pronunciation variation rules. 
         [0063]    The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the invention, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.