Source: https://patents.google.com/patent/US20140297281A1/en
Timestamp: 2020-02-16 19:48:57
Document Index: 161610019

Matched Legal Cases: ['Application No. 2013', 'art 50', 'art 50', 'art 55', 'art 55', 'art.\n2', 'art.\n10', 'art.\n12']

US20140297281A1 - Speech processing method, device and system - Google Patents
Speech processing method, device and system Download PDF
US20140297281A1
US20140297281A1 US14/196,202 US201414196202A US2014297281A1 US 20140297281 A1 US20140297281 A1 US 20140297281A1 US 201414196202 A US201414196202 A US 201414196202A US 2014297281 A1 US2014297281 A1 US 2014297281A1
word candidate
US14/196,202
Taro Togawa
2013-03-28 Priority to JP2013070682A priority Critical patent/JP6221301B2/en
2013-03-28 Priority to JP2013-070682 priority
2014-03-04 Application filed by Fujitsu Ltd filed Critical Fujitsu Ltd
2014-03-06 Assigned to FUJITSU LIMITED reassignment FUJITSU LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: OTANI, TAKESHI, Shioda, Chisato, TOGAWA, TARO
2014-10-02 Publication of US20140297281A1 publication Critical patent/US20140297281A1/en
230000004044 response Effects 0 description 146
A speech processing method executed by a computer, the speech processing method includes: extracting, based on speech recognition for an input speech data, a plurality of word candidates including a first word candidate and a second word candidate from a memory, the plurality of word candidates being candidates for a word corresponding to the input speech data; determining at least one different part between the first word candidate and the second word candidate based on a comparison between the first word candidate and the second word candidate; and outputting the first word candidate with emphasis on the at least one different part.
This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2013-070682, filed on Mar. 28, 2013, the entire contents of which are incorporated herein by reference.
The embodiments discussed herein are related to a technique for processing speech.
There is a speech interaction system that repeatedly executes an interaction with a user and executes various tasks such as a search of information. The speech interaction system uses a speech recognition technique for converting speech input from a user into a word. The existing speech interaction system does not independently determine whether or not a speech recognition result is correct. Thus, the speech interaction system displays the speech recognition result on a display or the like and prompts the user to confirm whether or not the speech recognition result is correct.
If the speech interaction system frequently prompts the user to confirm whether or not a speech recognition result is correct, a load applied to the user increases. Thus, there is a demand to efficiently confirm whether or not a speech recognition result is correct.
For example, there is a conventional technique for slowly reproducing an overall word that has a low degree of reliability for speech recognition and prompting a user to confirm whether or not a speech recognition result is correct. For example, if the user says that “what is the weather in Okayama prefecture?”, the speech interaction system recognizes that “what is the weather in Wakayama prefecture?”, and the degree of reliability of the word “Wakayama” is low, the speech interaction system slowly reproduces “Wakayama” included in the speech recognition result and prompts the user to confirm whether or not the speech recognition result is correct. The techniques are disclosed in, for example, Japanese Laid-open Patent Publication Nos. 2003-208196 and 2006-133478.
According to an aspect of the invention, a speech processing method executed by a computer, the speech processing method includes: extracting, based on speech recognition for an input speech data, a plurality of word candidates including a first word candidate and a second word candidate from a memory, the plurality of word candidates being candidates for a word corresponding to the input speech data; determining at least one different part between the first word candidate and the second word candidate based on a comparison between the first word candidate and the second word candidate; and outputting the first word candidate with emphasis on the at least one different part.
FIG. 1 is a diagram illustrating the configuration of a speech processing apparatus according to the first embodiment;
FIG. 2 is a diagram illustrating the configuration of a selector according to the first embodiment;
FIG. 3 is a diagram describing a process that is executed by a likely candidate extractor according to the first embodiment;
FIG. 4 is a first diagram describing a process that is executed by an evaluator according to the first embodiment;
FIG. 5 is a second diagram describing the process that is executed by the evaluator according to the first embodiment;
FIG. 6 is a third diagram describing the process that is executed by the evaluator according to the first embodiment;
FIG. 7 is a diagram illustrating the configuration of an emphasis controller according to the first embodiment;
FIG. 8 is a diagram describing a process that is executed by a mora position matching section according to the first embodiment;
FIG. 9 is a flowchart of a process procedure of the speech processing apparatus according to the first embodiment;
FIG. 10 is a flowchart of a process procedure of the selector according to the first embodiment;
FIG. 11 is a diagram illustrating the configuration of a speech processing apparatus according to the second embodiment;
FIG. 12 is a diagram illustrating the configuration of a selector according to the second embodiment;
FIG. 13 is a diagram describing a process that is executed by a likely candidate extractor according to the second embodiment;
FIG. 14 is a diagram illustrating the configuration of a speech processing apparatus according to the third embodiment;
FIG. 15 is a diagram illustrating the configuration of a selector according to the third embodiment;
FIG. 16 is a diagram illustrating an example of word candidates extracted by a likely candidate extractor according to the third embodiment and degrees of reliability;
FIG. 17 is a first diagram describing a process that is executed by an evaluator according to the third embodiment;
FIG. 18 is a second diagram describing the process that is executed by the evaluator according to the third embodiment;
FIG. 19 is a third diagram describing the process that is executed by the evaluator according to the third embodiment;
FIG. 20 is a diagram illustrating the configuration of an emphasis controller according to the third embodiment;
FIG. 21 is a diagram describing a process that is executed by a mora position matching section according to the third embodiment;
FIG. 22 is a diagram illustrating an example of a speech processing system according to the fourth embodiment;
FIG. 23 is a diagram illustrating the configuration of a server according to the fourth embodiment; and
FIG. 24 is a diagram illustrating an example of a computer that executes a speech processing program.
The aforementioned conventional techniques have a problem that an error of a speech recognition result is not easily found.
Regarding the conventional techniques, when an overall word that has a low degree of reliability for speech recognition is slowly reproduced, it is difficult to distinguish between the reproduced word and a correct recognition result and a user may not determine whether or not the result has been erroneously recognized. For example, regarding the aforementioned example, even if “Wakayama prefecture” that has a low degree of reliability is slowly reproduced, and the user listens to the overall words, “Wakayama prefecture” sounds similar to “Okayama prefecture” and the user may not determine whether the reproduced word is “Wakayama” or “Okayama”.
According to an aspect, the embodiments are intended to solve the aforementioned problems, and an object of the embodiments is to cause a user to easily find an error of a speech recognition result.
Hereinafter, the embodiments of a speech processing apparatus disclosed herein, a speech processing system disclosed herein, and a speech processing method disclosed herein are described in detail with reference to the accompanying drawings. However, the speech processing apparatus disclosed herein, the speech processing system disclosed herein, and the speech processing method disclosed herein are not limited to the embodiments.
A speech processing apparatus according to the first embodiment is described. FIG. 1 is a diagram illustrating the configuration of the speech processing apparatus according to the first embodiment. As illustrated in FIG. 1, the speech processing apparatus 100 has a speech recognizer 110, a selector 120, and a response speech generator 130. The response speech generator 130 has a response sentence generator 130 a, an emphasis controller 130 b, and a text synthesizer 130 c.
The speech recognizer 110 is a processor that executes speech recognition so as to convert speech input from a microphone or the like into a word and extracts a plurality of word candidates corresponding to the speech. The speech recognizer 110 calculates degrees of reliability of the word candidates. The speech recognizer 110 outputs, to the selector 120 and the response sentence generator 130 a, information in which the word candidates are associated with the degrees of reliability. In the following description, speech or speech that is input from the microphone or the like is referred to as an input speech.
An example of a process that is executed by the speech recognizer 110 is described in detail. The speech recognizer 110 holds a reference table in which a plurality of words are associated with reference patterns of speech corresponding to the words. The speech recognizer 110 calculates a characteristic vector of input speech on the basis of a frequency characteristic of the input speech, compares the calculated characteristic vector with the reference patterns of the reference table, and calculates degrees of similarities between the characteristic vector and the reference patterns. The degrees of the similarities between the characteristic vector and the reference patterns are referred to as degrees of reliability.
The speech recognizer 110 extracts, as a word candidate, a reference pattern other than a reference pattern of which a degree of reliability with respect to the characteristic vector is very close to 0. For example, the speech recognizer 110 extracts, as a word candidate, a reference pattern of which a degree of reliability with respect to the characteristic vector is equal to or larger than 0.1. The speech recognizer 110 outputs, to the selector 120 and the response speech generator 130, information in which the extracted word candidate is associated with the degree of reliability.
A process that is executed by the speech recognizer 110 to calculate degrees of reliability of the word candidates is not limited to the aforementioned process and may be executed using any known technique. For example, the speech recognizer 110 may calculate degrees of reliability of the word candidates using the technique disclosed in Japanese Laid-open Patent Publication No. 4-255900.
The selector 120 is a processor that selects a part corresponding to a difference between the plurality of word candidates. FIG. 2 is a diagram illustrating the configuration of the selector according to the first embodiment. As illustrated in FIG. 2, the selector 120 has a likely candidate extractor 120 a and an evaluator 120 b.
The likely candidate extractor 120 a extracts, on the basis of the degrees of reliability of the plurality of word candidates, a word candidate of which a degree of reliability is equal to or larger than a threshold. The likely candidate extractor 120 a outputs a combination of the extracted word candidate and the degree of reliability of the extracted word candidate to the evaluator 120 b.
FIG. 3 is a diagram describing a process that is executed by the likely candidate extractor according to the first embodiment. For example, it is assumed that relationships between the word candidates received from the speech recognizer 110 and the degrees of reliability are relationships illustrated in FIG. 3 and that the predetermined threshold is “0.6”. In this case, the likely candidate extractor 120 a extracts combinations of word candidates of candidate numbers 1 to 3 and degrees of reliability of the word candidates. The likely candidate extractor 120 a outputs, to the evaluator 120 b, information of the combinations of the extracted word candidates and the degrees of reliability of the extracted word candidates.
The evaluator 120 b is a processor that compares the word candidates with each other and selects a part corresponding to a difference between the word candidates. In the following description, a word candidate of which a degree of reliability is largest is referred to as a first word candidate, and other word candidates are referred to as second word candidates. In an example illustrated in FIG. 3, a word candidate “Wakayama” of which a degree of reliability is “0.80” is a first word candidate, and a word candidate “Okayama” of which a degree of reliability is “0.75” and a word candidate “Toyama” of which a degree of reliability is “0.65” are second word candidates.
The evaluator 120 b calculates scores for matching the first word candidate with the second word candidates, sums the calculated matching scores, and thereby calculates a final matching score for the first word candidate. For example, the evaluator 120 b compares the first word candidate “Wakayama” with the second word candidate “Okayama” and calculates a matching score. In addition, the evaluator 120 b compares the first word candidate “Wakayama” with the other second word candidate “Toyama” and calculates a matching score. The evaluator 120 b sums the calculated matching scores and thereby calculates a final matching score for the first word candidate.
The evaluator 120 b uses DP matching to calculate the matching scores, for example. FIGS. 4, 5, and 6 are diagrams describing a process that is executed by the evaluator 120 b according to the first embodiment. First, the process is described with reference to FIG. 4. FIG. 4 describes the process of comparing the first word candidate “Wakayama” with the second word candidate “Okayama”. The evaluator 120 b compares portions or characters of the first word candidate with portions of characters of the second word candidate. If a portion or character of the first word candidate matches a portion or character of the second word candidate, the evaluator 120 b provides a score “0” to the character of the first word candidate. If the portion or character of the first word candidate does not match the portion or character of the second word candidate, the evaluator 120 b provides a score “−1” to the portion or character of the first word candidate. In this manner, the evaluator 120 b generates a table 10 a by providing the scores.
The evaluator 120 b identifies scores for the characters of the first word candidate by selecting a path on which larger scores among scores for the characters of the first word candidate exist on the basis of the table 10 a on a priority basis. In an example illustrated in FIG. 4, a path 11 a is selected and scores for the characters of the first word candidate are indicated in a score table 20 a. Specifically, a score for “wa” is “−1” and scores for “ka”, “ya”, and “ma” are “0”.
The process is described with reference to FIG. 5. FIG. 5 describes the process of comparing the first word candidate “Wakayama” with the second word candidate “Toyama”. The evaluator 120 b compares the characters of the first word candidate with characters of the second word candidate. If a character of the first word candidate matches a character of the second word candidate, the evaluator 120 b provides a score “0” to the character of the first word candidate. If the character of the first word candidate does not match the character of the second word candidate, the evaluator 120 b provides a score “−1” to the character of the first word candidate. In this manner, the evaluator 120 b generates a table 10 b by providing the scores.
The evaluator 120 b identifies scores for the characters of the first word candidate by selecting a path on which larger scores among scores for the characters of the first word candidate exist on the basis of the table 10 b on a priority basis. In an example illustrated in FIG. 5, a path 11 b is selected and scores for the characters of the first word candidate are indicated in a score table 20 b. Specifically, scores for “wa” and “ka” are “−1” and scores for “ya” and “ma” are “0”.
The process is described with reference to FIG. 6. The evaluator 120 b sums the score table 20 a and the score table 20 b for each of the characters of the first word candidate and thereby calculates a score table 30 for the first word candidate.
The evaluator 120 b selects, on the basis of the score table 30, a part included in the first word candidate and corresponding to a difference between the first word candidate and the second word candidates. For example, the evaluator 120 b selects a score that is smaller than “0” from among scores of the score table 30. Then, the evaluator 120 b selects, as the part corresponding to the difference, a character corresponding to the selected score. In an example illustrated in FIG. 6, the evaluator 120 b selects, as the part corresponding to the difference, “wa” and “ka” from among the characters “wa”, “ka”, “ya”, and “ma” of the first word candidate. The evaluator 120 b outputs information of the selected part to the emphasis controller 130 b.
Return to FIG. 1. The response sentence generator 130 a is a processor that generates a response sentence that is used to check with the user whether or not a speech recognition result is correct. For example, the response sentence generator 130 a holds templates of character strings of multiple types and generates a response sentence by synthesizing a word candidate received from the speech recognizer 110 with a template. The response sentence generator 130 a outputs information of the generated response sentence to the emphasis controller 130 a and the text synthesizer 130 c.
For example, when receiving a plurality of word candidates, the response sentence generator 130 a selects a word candidate having the largest degree of reliability and generates audio such as a response sentence. For example, if the word candidate of which the degree of reliability is largest is “Wakayama”, the response sentence generator 130 a synthesizes the word candidate with a template indicating “Is it ** ?” and generates a response sentence “Is it Wakayama?”.
The emphasis controller 130 b is a processor that selects a part included in the response sentence and to be distinguished or emphasized and notifies the text synthesizer 130 c of the selected part to be emphasized or distinguished from the rest of the selected word candidate and a parameter for emphasizing the part. FIG. 7 is a diagram illustrating the configuration of the emphasis controller according to the first embodiment. As illustrated in FIG. 7, the emphasis controller 130 b has a mora position matching section 131 and an emphasis parameter setting section 132.
The mora position matching section 131 is a processor that selects, on the basis of the information received from the evaluator 120 b and indicating the part corresponding to the difference, a part included in the response sentence to be emphasized. FIG. 8 is a diagram describing a process that is executed by the mora position matching section 131 according to the first embodiment. As illustrated in FIG. 8, the mora position matching section 131 crosschecks a start mora position 40 a of a response sentence 40 with a part 50 a included in a word candidate 50 and corresponding to the differences and thereby calculates a part included in the response sentence 40 and to be emphasized. In an example illustrated in FIG. 8, the first and second characters that are included in the response sentence 40 and correspond to the part 50 a corresponding to the differences are “wa” and “ka”, respectively. Thus, the part to be emphasized is moras 1 and 2.
The emphasis parameter setting section 132 outputs a parameter indicating a set amplitude amount to the text synthesizer 130 c. For example, the emphasis parameter setting section 132 outputs, to the text synthesizer 130 c, information indicating that “the part to be emphasized is amplified by 10 dB”.
The text synthesizer 130 c is a processor that generates, on the basis of the information of the response sentence, information of the part to be emphasized, and the parameter for the emphasis, response speech corresponding to the response sentence and including emphasized speech of the part and outputs the generated response speech. For example, the text synthesizer 130 c executes language analysis on the response sentence, identifies prosodies corresponding to words of the response sentence, synthesizes the identified prosodies, and thereby generates the response speech. The text synthesizer 130 c emphasizes a prosody of speech corresponding to a character of the part included in the response speech and to be emphasized and thereby generates the response speech including emphasized speech of the part.
For example, if the part to be emphasized is the “moras 1 and 2” and the parameter indicates that “the part to be emphasized will be amplified by 10 dB”, the text synthesizer 130 c amplifies, by 10 dB, power of speech of a part “Waka” included in the response sentence “Is it Wakayama?” and thereby generates response speech of the response sentence. The response speech generated by the text synthesizer 130 c is output from a speaker or the like. For example, the response speech is output, while the speech of the part “Waka” of the response sentence “Is it Wakayama?” is more emphasized than the other words of the response sentence.
If a plurality of word candidates are not extracted by the selector 120, the response speech generator 130 converts information of a response sentence into response speech without changing the response sentence and outputs the response speech.
Next, a process procedure of the speech processing apparatus 100 according to the first embodiment is described. FIG. 9 is a flowchart of the process procedure of the speech processing apparatus according to the first embodiment. The process procedure illustrated in FIG. 9 is executed when the speech processing apparatus 100 receives input speech. As illustrated in FIG. 9, the speech processing apparatus 100 receives input speech (in step S101), executes the speech recognition, and extracts word candidates (in step S102).
The speech processing apparatus 100 calculates degrees of reliability of the word candidates (in step S103) and selects word candidates of which degrees of reliability are equal to or larger than a predetermined value (in step S104). The speech processing apparatus 100 generates a response sentence (in step S105) and selects a part corresponding to a difference between the selected word candidates (in step S106).
The speech processing apparatus 100 sets a parameter (in step S107) and executes the language analysis (in step S108). The speech processing apparatus 100 generates prosodies (in step S109) and changes a prosody of a part to be emphasized (in step S110). The speech processing apparatus 100 executes waveform processing (in step S111) and outputs response speech (in step S112).
Next, an example of a process procedure of the selector 120 illustrated in FIG. 1 is described. FIG. 10 is a flowchart of the process procedure of the selector according to the first embodiment. The selector 120 extracts, from a plurality of word candidates, a word candidate of which a degree of reliability is equal to or larger than a predetermined value (in step S201).
The selector 120 determines whether or not the number of word candidates is two or more (in step S202). If the number of word candidates is not two or more (No in step S202), the selector 120 determines that a part corresponding to a difference does not exist (in step S203).
If the number of word candidates is two or more (Yes in step S202), the selector 120 calculates matching scores for second word candidates with respect to a first word candidate (in step S204). The selector 120 sums the scores for the word candidates (in step S205). The selector 120 selects, as a part corresponding to a difference between the word candidates, a part for which the summed score is low (in step S206).
Next, effects of the speech processing apparatus 100 according to the first embodiment are described. The speech processing apparatus 100 selects, on the basis of a plurality of word candidates recognized by the speech recognizer 110, a part corresponding to a difference between the word candidates. The speech processing apparatus 100 outputs response speech including speech of which the volume has been increased and that corresponds to the part corresponding to the difference between the word candidates. In this manner, the speech processing apparatus 100 according to the first embodiment emphasizes only speech of a part corresponding to a difference between word candidates without emphasizing speech of an overall word and outputs response speech including the emphasized speech of the part. Thus, an error of a speech recognition result may be easily found. In addition, if this technique is applied to a speech interaction system, the user may easily notice an erroneously recognized part and correctly pronounce a phrase, and the efficiency of an interaction executed to correct the erroneous recognition may be improved.
A speech processing apparatus according to the second embodiment is described below. FIG. 11 is a diagram illustrating the configuration of the speech processing apparatus according to the second embodiment. As illustrated in FIG. 11, the speech processing apparatus 200 has a speech recognizer 210, a selector 220, and response speech generator 230. The response speech generator 230 has a response sentence generator 230 a, an emphasis controller 230 b, and a text synthesizer 230 c.
The speech recognizer 210 is a processor that executes the speech recognition so as to convert speech input from a microphone or the like into a word and extracts a plurality of word candidates corresponding to the speech. In addition, the speech recognizer 210 calculates degrees of reliability of the word candidates. The speech recognizer 210 outputs, to the selector 220 and the response speech generator 230, information in which the word candidates are associated with the degrees of reliability. A specific description of the speech recognizer 210 is the same as or similar to the description of the speech recognizer 110 according to the first embodiment.
The selector 220 is a processor that selects a part corresponding to a difference between the plurality of word candidates. FIG. 12 is a diagram illustrating the configuration of the selector according to the second embodiment. As illustrated in FIG. 12, the selector 220 has a likely candidate extractor 220 a and an evaluator 220 b.
The likely candidate extractor 220 a extracts, on the basis of degrees of reliability of the plurality of word candidates, a word candidate of which a degree of reliability is different by a predetermined threshold or less from the largest degree of reliability. The likely candidate extractor 220 a outputs a combination of the extracted word candidate and the degree of reliability of the extracted word candidate to the evaluator 220 b.
FIG. 13 is a diagram describing a process that is executed by the likely candidate extractor according to the second embodiment. In an example illustrated in FIG. 13, candidate numbers, word candidates, degrees of reliability, and differences between the degrees of reliability and the largest degree of reliability are associated with each other. If the predetermined threshold is “0.2”, word candidates of which degrees of reliability are different by the predetermined threshold or less from the largest degree of reliability are word candidates of candidate numbers 1 to 3. Thus, the likely candidate extractor 220 a outputs information of combinations of the word candidates of the candidate numbers 1 to 3 and the degrees of reliability of the word candidates to the evaluator 220 b.
The evaluator 220 b is a processor that compares the word candidates with each other and selects a part corresponding to a difference between the word candidates. In the same manner as the first embodiment, a word candidate of which a degree of reliability is largest is referred to as a first word candidate, and other word candidates are referred to as second word candidates. The evaluator 220 b executes the same process as the evaluator 120 b described in the first embodiment, selects the part corresponding to the difference between the word candidates, and outputs information of the selected part corresponding to the difference to the emphasis controller 230 b.
The response sentence generator 230 a is a processor that generates a response sentence that is used to prompt the user to check whether or not a speech recognition result is correct. A process that is executed by the response sentence generator 230 a to generate the response sentence is the same as or similar to the process executed by the response sentence generator 130 a described in the first embodiment. The response sentence generator 230 a outputs information of the generated response sentence to the emphasis controller 230 b and the text synthesizer 230 c.
The emphasis controller 230 b is a processor that selects a part included in the response sentence and to be emphasized and notifies the text synthesizer 230 c of the selected part to be emphasized and a parameter for emphasizing the selected part. The emphasis controller 230 b identifies the part (to be emphasized) in the same manner as the emphasis controller 130 b described in the first embodiment. The emphasis controller 230 b outputs, to the text synthesizer 230 c, information indicating that “the persistence length of the part to be emphasized will be doubled” as the parameter.
The text synthesizer 230 c is a processor that generates, on the basis of the information of the response sentence, the information of the part to be emphasized, and the parameter for emphasizing the part, response speech corresponding to the response sentence and including emphasized speech of the part and outputs the generated response speech. For example, the text synthesizer 230 c executes the language analysis on the response sentence, identifies prosodies corresponding to words of the response sentence, synthesizes the identified prosodies, and thereby generates the response speech. The text synthesizer 230 c emphasizes a prosody of speech corresponding to a character of the part included in the response speech and to be emphasized and thereby generates the response speech including the emphasized speech of the part.
For example, if the part to be emphasized is the “moras 1 and 2” and the parameter indicates that “the persistence length of the part to be emphasized will be doubled”, the text synthesizer 230 c doubles the persistence length of a prosodic part of the part “Waka” included in the response sentence “Is it Wakayama?” and generates response speech of the response sentence. The response speech generated by the text synthesizer 230 c is output from a speaker or the like. The part “Waka” included in the response sentence “Is it Wakayama?” is output for a longer time period than the other part of the response sentence and is thereby emphasized.
Next, effects of the speech processing apparatus 200 according to the second embodiment are described. The speech processing apparatus 200 selects, on the basis of a plurality of word candidates recognized by the speech recognizer 210, a part corresponding to a difference between the word candidates. The speech processing apparatus 200 outputs response speech including speech of the part that corresponds to the difference between the word candidates and of which the persistence length has been increased. Since the speech processing apparatus 200 according to the second embodiment increases only the persistence length of a part corresponding to a difference between word candidates without increasing the persistence length of an overall word and outputs response speech including speech of the part corresponding to the difference, an error of a speech recognition result may be easily found. In addition, if this technique is applied to the speech interaction system, the user may easily notice an erroneously recognized part and correctly pronounce a phrase, and the efficiency of an interaction executed to correct the erroneous recognition may be improved.
The speech processing apparatus 200 according to the second embodiment may use information indicating that “the pitch of the part corresponding to the difference will be doubled” as the parameter. Then, the speech processing apparatus 200 may emphasize the part corresponding to the difference. The pitch corresponds to a fundamental frequency, for example. If the part to be emphasized is the “moras 1 and 2” and the parameter indicates that “the pitch of the part to be emphasized will be doubled”, the text synthesizer 230 c doubles the pitch of the prosodic part of the part “Waka” included in the response sentence “Is it Wakayama?” and thereby generates response speech including emphasized speech that corresponds to the part and is lower than normal speech. Since the speech processing apparatus 200 according to the second embodiment lowers only the speech pitch of the part corresponding to the difference and outputs the response speech including the emphasized speech of the part, an error of a speech recognition result may be easily found. The speech processing apparatus 200 may decrease the pitch of the part by ½ and emphasize the speech of the part.
A speech processing apparatus according to the third embodiment is described. FIG. 14 is a diagram illustrating the configuration of the speech processing apparatus according to the third embodiment. As illustrated in FIG. 14, the speech processing apparatus 300 has a speech recognizer 310, a selector 320, response speech generator 330. The response speech generator 330 has a response sentence generator 330 a, an emphasis controller 330 b, and a text synthesizer 330 c.
The speech recognizer 310 is a processor that executes the speech recognition so as to convert speech input from a microphone or the like into a word and extracts a plurality of word candidates corresponding to the speech. In addition, the speech recognizer 310 calculates degrees of reliability of the word candidates. The speech recognizer 310 outputs, to the selector 320 and the response sentence generator 330 a, information in which the word candidates are associated with the degrees of reliability. In the following description, speech that is input from the microphone or the like is referred to as input speech.
An example of a process that is executed by the speech recognizer 310 is described in detail. The speech recognizer 310 holds a reference table in which a plurality of words are associated with reference patterns of speech corresponding to the words. The speech recognizer 310 calculates a characteristic vector of input speech on the basis of a frequency characteristic of the input speech, compares the calculated characteristic vector with the reference patterns of the reference table, and calculates degrees of similarities between the characteristic vector and the reference patterns. The degrees of the similarities between the characteristic vector and the reference patterns are referred to as degrees of reliability.
The speech recognizer 310 extracts, as a word candidate, a reference pattern other than a reference pattern of which a degree of reliability with respect to the characteristic vector is very close to 0. For example, the speech recognizer 310 extracts, as a word candidate, a reference pattern of which a degree of reliability with respect to the characteristic vector is equal to or larger than 0.1. The speech recognizer 310 outputs, to the selector 320 and the response speech generator 330, information in which the extracted word candidate is associated with the degree of reliability.
The selector 320 is a processor that selects a part corresponding to a difference between the plurality of word candidates. FIG. 15 is a diagram illustrating the configuration of the selector according to the third embodiment. As illustrated in FIG. 15, the selector 320 has a likely candidate extractor 320 a and an evaluator 320 b.
The likely candidate extractor 320 a extracts, on the basis of the degrees of reliability of the plurality of word candidates, a word candidate of which a degree of reliability is equal to or larger than a predetermined threshold. The likely candidate extractor 320 a outputs information of a combination of the extracted word candidate and the degree of reliability of the word candidate to the evaluator 320 b. A word candidate of which a degree of reliability is largest is referred to as a first word candidate, while the other word candidates are referred to as second word candidates.
FIG. 16 is a diagram illustrating an example of the word candidates extracted by the likely candidate extractor according to the third embodiment and the degrees of reliability of the extracted word candidates. As illustrated in FIG. 16, syllables of a first word candidate “seven” are “sev” and “en”. Syllables of a second word candidate “eleven” are “e”, “lev”, and “en”. Syllables of another second word candidate “seventeen” are “sev”, “en”, and “teen”.
The evaluator 320 b calculates scores for matching the first word candidate with the second word candidates, sums the calculated matching scores, and calculates a final matching score for the first word candidate. For example, the evaluator 320 b compares the first word candidate “seven” with the second word candidate “eleven” and calculates a matching score. In addition, the evaluator 320 b compares the first word candidate “seven” with the second word candidate “seventeen” and calculates a matching score. The evaluator 320 b sums the matching scores and calculates a final matching score for the first word candidate.
The evaluator 320 b uses DP matching to calculate the matching scores, for example. FIGS. 17, 18, and 19 are diagrams describing a process that is executed by the evaluator according to the third embodiment. First, the process is described with reference to FIG. 17. FIG. 17 describes the process of comparing the first word candidate “seven” with the second word candidate “eleven”. The evaluator 320 b compares characters of the first word candidates with characters of the second word candidate. If a character of the first word candidate matches a character of the second word candidate, the evaluator 320 b provides a score “0” to the character of the first word candidate. If the character of the first word candidate does not match the character of the second word candidate, the evaluator 320 b provides a score “−1” to the character of the first word candidate. In this manner, the evaluator 320 b generates a table 10 c by providing the scores.
The evaluator 320 b identifies scores for the characters of the first word candidate by selecting a path on which larger scores among scores for the characters of the first word candidate exist on the basis of the table 10 c on a priority basis. In an example illustrated in FIG. 17, a path 11 c is selected and scores for the characters of the first word candidate are indicated in a score table 20 c. Specifically, a score for “s” is “−1” and scores for “e”, “v”, “e”, and “n” are “0”.
The process is described with reference to FIG. 18. FIG. 18 illustrates the process of comparing the first word candidate “seven” with the second word candidate “seventeen”. The evaluator 320 b compares the characters of the first word candidate with the characters of the second word candidate. If a character of the first word candidate matches a character of the second word candidate, the evaluator 320 b provides the score “0”. If the character of the first word candidate does not match the character of the second word candidate, the evaluator 320 b provides the score “−1”. In this manner, the evaluator 320 b generates a table 10 d by providing the scores. If the number of the characters of the first word candidate is smaller than the number of characters of a second word candidate, the evaluator 320 b compares the first word candidate with the second word candidate for the number of the characters of the first word candidate. For example, if the first word candidate “seven” is to be compared with the second word candidate “seventeen”, the evaluator 320 b compares the characters of the first word candidate with characters “seven” included in the characters of the second word candidate “seventeen”.
The evaluator 320 b identifies scores for the characters of the first word candidate by selecting a path on which larger scores among scores for the characters of the first word candidate exist on the basis of the table 10 d on a priority basis. In an example illustrated in FIG. 18, a path 11 d is selected and scores for the characters of the first word candidate are indicated in a score table 20 d. Specifically, the scores for “s”, “e”, “v”, “e”, and “n” are “0”.
The process is described with reference to FIG. 19. The evaluator 320 b sums the score table 20 c and the score table 20 d for each of the characters of the first word candidate and thereby calculates a score table 35 for the first word candidate.
The evaluator 320 b selects, on the basis of the score table 35, a part corresponding to a difference between the first word candidate and the second word candidates. For example, the evaluator 320 b selects a score that is smaller than “0” from among scores of the score table 35. Then, the evaluator 320 b selects, as the part corresponding to the difference, a character corresponding to the selected score. In an example illustrated in FIG. 19, the evaluator 320 b selects, as the part corresponding to the difference, a character “s” from among the characters of the first word candidate “seven”. The evaluator 320 b outputs information of the part corresponding to the difference to the emphasis controller 330 b.
Return to FIG. 14. The response sentence generator 330 a is a processor that generates a response sentence that is used to prompt the user to check whether or not a speech recognition result is correct. For example, the response sentence generator 330 a holds templates of character strings of multiple types and generates a response sentence by synthesizing a word candidate received from the speech recognizer 310 with a template. The response sentence generator 330 a outputs information of the generated response sentence to the emphasis controller 330 b and the text synthesizer 330 c.
For example, when receiving a plurality of word candidates, the response sentence generator 330 a selects a word candidate having the largest degree of reliability and generates a response sentence. For example, if the word candidate of which the degree of reliability is largest is “seven”, the response sentence generator 330 a synthesizes the word candidate “seven” with a template “o'clock?” and generates a response sentence “Seven o'clock?”.
The emphasis controller 330 b is a processor that selects a part included in the response sentence and to be emphasized and notifies the text synthesizer 330 c of the selected part to be emphasized and a parameter for emphasizing the part. FIG. 20 is a diagram illustrating the configuration of the emphasis controller according to the third embodiment. As illustrated in FIG. 20, the emphasis controller 330 b has a mora position matching section 331 and an emphasis parameter setting section 332.
The mora position matching section 331 is a processor that selects, on the basis of the information received from the evaluator 320 b and indicating the part corresponding to the difference, a part included in the response sentence and to be emphasized. FIG. 21 is a diagram describing a process that is executed by the mora position matching section according to the third embodiment. As illustrated in FIG. 21, the mora position matching section 331 crosschecks a start mora position 45 a of a response sentence 45 with a part 55 a included in a word candidate 55 and corresponding to a difference between word candidates and calculates a part included in the response sentence 45 and to be emphasized. In an example illustrated in FIG. 21, a character that is included in the response sentence 45 and corresponds to the part 55 a corresponding to the difference is the first character “s”. Thus, the part to be emphasized is a mora 1. The mora position matching section 331 may identify a part to be emphasized on a syllable basis. For example, since the first character “s” is included in the syllable “sev”, the mora position matching section 331 may identify the characters “sev” as the part to be emphasized. In this case, the part to be emphasized is moras 1 to 3.
The emphasis parameter setting section 332 outputs a parameter indicating a set amplitude amount to the text synthesizer 330 c. For example, the emphasis parameter setting section 332 outputs, to the text synthesizer 330 c, information indicating that “the part to be emphasized is amplified by 10 dB”.
The text synthesizer 330 c is a processor that generates, on the basis of the information of the response sentence, information of the part to be emphasized, and the parameter for the emphasis, response speech including emphasized speech of the part and corresponding to the response sentence and outputs the generated response speech. For example, the text synthesizer 330 c executes the language analysis on the response sentence, identifies prosodies corresponding to words of the response sentence, synthesizes the identified prosodies, and generates the response speech. The text synthesizer 330 c emphasizes a prosody of speech corresponding to a character of the part to be emphasized and generates the response speech including the emphasized speech of the part.
For example, if the part to be emphasized is the “moras 1 to 3” and the parameter indicates that “the part to be emphasized will be amplified by 10 dB”, the text synthesizer 330 c amplifies, by 10 dB, power of speech of the part “Sev” included in the response sentence “Seven o'clock?” and generates response speech of the response sentence. The response speech generated by the text synthesizer 330 c is output from a speaker or the like. For example, the response speech is output, while the speech of the part “Sev” included in the response sentence “Seven o'clock?” is more emphasized than the other words.
The parameter for emphasizing the part is not limited to the aforementioned parameter. For example, if the parameter indicates that “the persistence length of the part to be emphasized will be doubled”, the text synthesizer 330 c doubles the persistence length of a prosodic part of the part “Sev” of the response sentence “Seven o'clock?” and generates response speech of the response sentence. For example, if the parameter indicates that “the pitch of the part to be emphasized will be doubled”, the text synthesizer 330 c doubles the pitch of the prosodic part of the part “Sev” of the response sentence “Seven o'clock?” and thereby generates response speech including speech that corresponds to the emphasized part and is lower than normal speech.
Next, effects of the speech processing apparatus 300 according to the third embodiment are described. The speech processing apparatus 300 selects, on the basis of a plurality of word candidates recognized by the speech recognizer 310, a part corresponding to a difference between the plurality of word candidates. The speech processing apparatus 300 outputs response speech including the part that corresponds to the difference between the plurality of word candidates and of which the volume has been increased. Since the speech processing apparatus 300 according to the third embodiment emphasizes only speech of a part corresponding to a difference between word candidates without emphasizing speech of an overall word and outputs response speech including the emphasized speech of the part, an error of a speech recognition result may be easily found. In addition, if this technique is applied to the speech interaction system, the user may easily notice an erroneously recognized part and correctly pronounce a phrase, and the efficiency of an interaction executed to correct the erroneous recognition may be improved.
A speech processing system according to the fourth embodiment is described below. FIG. 22 is a diagram illustrating an example of the speech processing system according to the fourth embodiment. As illustrated in FIG. 22, the speech processing system has a terminal apparatus 400 and a server 500. The terminal apparatus 400 and the server 500 are connected to each other through a network 80.
The terminal apparatus 400 uses a microphone or the like to receive speech from a user and transmits information of the received speech to the server 500. The terminal apparatus 400 receives information of response speech from the server 500 and outputs the received response speech from a speaker or the like.
The server 500 has the same functions as the speech processing apparatuses according to the first to third embodiments. FIG. 23 is a diagram illustrating the configuration of the server according to the fourth embodiment. As illustrated in FIG. 23, the server 500 has a communication controller 500 a and a speech processor 500 b. The speech processor 500 b has a speech recognizer 510, a selector 520, and a response speech generator 530. The response speech generator 530 has a response sentence generator 530 a, an emphasis controller 530 b, and a text synthesizer 530 c.
The communication controller 500 a is a processor that executes data communication with the terminal apparatus 400. The communication controller 500 a outputs, to the speech recognizer 510, information of speech received from the terminal apparatus 400. In addition, the communication controller 500 a transmits, to the terminal apparatus 400, information of response speech output from the text synthesizer 530 c.
The speech recognizer 510 is a processor that receives information of speech from the communication controller 500 a, executes the speech recognition so as to convert the speech into a word, and extracts a plurality of word candidates corresponding to the speech. In addition, the speech recognizer 510 calculates degrees of reliability of the word candidates. The speech recognizer 510 outputs, to the selector 520 and the response sentence generator 530 a, information in which the word candidates are associated with the degrees of reliability.
The selector 520 is a processor that selects a part corresponding to a difference between the plurality of word candidates. A specific description of the selector 520 is the same as or similar to the descriptions of the selectors 120, 220, and 320 described in the first to third embodiments.
The response sentence generator 530 a is a processor that generates a response sentence that is used to prompt the user to check whether or not a speech recognition result is correct. A process that is executed by the response sentence generator 530 a to generate the response sentence is the same as or similar to the process executed by the response sentence generator 130 a according to the first embodiment. The response sentence generator 530 a outputs information of the generated response sentence to the emphasis controller 530 b and the text synthesizer 530 c.
The emphasis controller 530 b is a processor that selects a part included in the response sentence and to be emphasized and notifies the text synthesizer 530 c of the selected part to be emphasized and a parameter for emphasizing the part. The emphasis controller 530 b identifies the part to be emphasized in the same manner as the emphasis controller 130 b according to the first embodiment. The emphasis controller 530 b outputs, to the text synthesizer 530 c, information indicating that “the persistence length of the part to be emphasized will be doubled” as the parameter. The emphasis controller 530 b may output, to the text synthesizer 530 c, information indicating that “the part to be emphasized will be amplified by 10 dB” as the parameter. In the same manner as the second embodiment, the parameter may be the information indicating that “the persistence length of the part to be emphasized will be doubled” or the information indicating that “the pitch of the part to be emphasized will be doubled”.
The text synthesizer 530 c is a processor that generates, on the basis of the information of the response sentence, the information of the part to be emphasized, and the parameter for emphasizing the part, response speech of the response sentence including emphasized speech of the part and outputs the generated response speech. For example, the text synthesizer 530 c executes the language analysis on the response sentence, identifies prosodies corresponding to words of the response sentence, synthesizes the identified prosodies, and generates the response speech. The text synthesizer 530 c emphasizes a prosody of speech corresponding to a character of the part included in the response speech and to be emphasized and thereby generates the response speech including the emphasized speech of the part. The text synthesizer 530 c outputs information of the generated response speech to the communication controller 500 a.
Next, effects of the server 500 according to the fourth embodiment are described. The server 500 selects a part corresponding to a difference between a plurality of candidates recognized by the speech recognizer 510. The server 500 outputs response speech including speech of which the volume has been increased and that corresponds to the part corresponding to the difference between the word candidates. Since the server 500 according to the fourth embodiment emphasizes only speech of a part corresponding to a difference between word candidates without emphasizing speech of an overall word and outputs response speech including the emphasized speech of the part, an error of a speech recognition result may be easily found. If this technique is applied to the speech interaction system, the user may easily find an erroneously recognized part and correctly pronounce a phrase, and the efficiency of an interaction executed to correct the erroneous recognition may be improved.
Next, an example of a computer that executes a speech processing program that achieves the same functions as the speech processing apparatuses according to the first to third embodiments is described. FIG. 24 is a diagram illustrating the example of the computer that executes the speech processing program.
As illustrated in FIG. 24, a computer 600 has a CPU 601 for executing arithmetic processing of various types, an input device 602 for receiving an entry of data from a user, and a display 603. The computer 600 also has a reader 604 for reading the program and the like from a recording medium and an interface device 605 for transmitting and receiving data to and from another computer through a network. The computer 600 also has a RAM 606 for temporarily storing information of various types and a hard disk device 607. The devices 601 to 607 are connected to each other by a bus 608.
The hard disk device 607 has a speech recognition program 607 a, a selection program 607 b, and an output program 607 c. The CPU 601 reads the programs 607 a to 607 c and loads the programs 607 a to 607 c into the RAM 606.
The speech recognition program 607 a functions as a speech recognition process 606 a. The selection program 607 b functions as a selection process 606 b. The output program 607 c functions as an output process 606 c.
For example, the speech recognition process 606 a corresponds to the speech recognizers 110, 210, 310, and 510. The selection process 606 b corresponds to the selectors 120, 220, 320, and 520. The output process 606 c corresponds to the response speech generators 130, 230, 330, and 530.
The programs 607 a to 607 c may not be stored in the hard disk device 607. For example, the programs 607 a to 607 c may be stored in a “portable physical medium” that is inserted in the computer 600 and is, for example, a flexible disk (FD), a CD-ROM, a DVD, a magneto-optical disc, or an IC card. The computer 600 may read the programs 607 a to 607 c from the portable physical medium and execute the programs 607 a to 607 c.
1. A speech processing method executed by a computer, the speech processing method comprising:
extracting, based on speech recognition for an input speech data, a plurality of word candidates including a first word candidate and a second word candidate from a memory, the plurality of word candidates being candidates for a word corresponding to the input speech data;
determining at least one different part between the first word candidate and the second word candidate based on a comparison between the first word candidate and the second word candidate; and
outputting the first word candidate with emphasis on the at least one different part.
2. The speech processing method according to claim 1, further comprising:
calculating a degree of reliability indicating similarity with respect to the input speech data, regarding each of the plurality of word candidates; and
identifying, from among the plurality of word candidates, the first word candidate and the second word candidate having degree of reliability which are equal to or larger than a threshold.
3. The speech processing method according to claim 1, further comprising:
calculating a degree of reliability indicating similarity with respect to the input speech data, regarding each of the plurality of word candidates;
identifying, from among the plurality of word candidates, the first word candidate which has a degree of reliability which is the largest, and the second word candidate which has a degree of reliability which is different by a value that is smaller than a threshold from the largest degree of reliability.
4. The speech processing method according to claim 1, wherein the outputting outputs speech data of the first word candidate with the emphasis, the speech data being stored in the memory and associated with the first word candidate.
5. The speech processing method according to claim 4, wherein the speech data is output with a first strength for the at least one different part and a second strength for rest of the first word candidate, wherein the first strength is stronger than the second strength.
6. The speech processing method according to claim 4, wherein the speech data is output with a first reproduction speed for the at least one different part and a second reproduction speed for rest of the first word candidate, wherein the first reproduction speed is slower than the second reproduction speed.
7. The speech processing method according to claim 4, wherein the speech data is output with a first fundamental frequency for the at least one different part and a second fundamental frequency for rest of the first word candidate, wherein the first fundamental frequency is different from the second fundamental frequency.
8. The speech processing method according to claim 1, wherein the plurality of word candidate are character strings respectively, and the at least one different part are determined based on the comparison between a first character strings of the first word candidate and a second character strings of the second word candidate.
9. The speech processing method according to claim 8, wherein the determining identifies, based on the comparison, a first portion of the first character strings and second portion of the first character strings, the first portion including characters same with a part of the second character strings in same positions, and the second portion being the at least one different part.
10. The speech processing method according to claim 8, wherein the at least one different part is determined using dynamic programming matching for the first character strings and the second character strings.
11. A speech processing device comprising:
extract, based on speech recognition for an input speech data, a plurality of word candidates including a first word candidate and a second word candidate from the memory, the plurality of word candidates being candidates for a word corresponding to the input speech data,
determine at least one different part between the first word candidate and the second word candidate based on a comparison between the first word candidate and the second word candidate, and
output the first word candidate with emphasis on the at least one different part.
12. The speech processing device according to claim 11, wherein the processor is further configured to:
calculate a degree of reliability indicating similarity with respect to the input speech data, regarding each of the plurality of word candidates, and
identify, from among the plurality of word candidates, the first word candidate and the second word candidate having degree of reliability which are equal to or larger than a threshold.
13. The speech processing device according to claim 11, wherein the processor is further configured to:
calculate a degree of reliability indicating similarity with respect to the input speech data, regarding each of the plurality of word candidates,
identify, from among the plurality of word candidates, the first word candidate which has a degree of reliability which is the largest, and the second word candidate which has a degree of reliability which is different by a value that is smaller than a threshold from the largest degree of reliability.
14. The speech processing device according to claim 11, wherein the plurality of word candidate are character strings respectively, and the at least one different part are determined based on the comparison between a first character strings of the first word candidate and a second character strings of the second word candidate.
15. The speech processing device according to claim 14, wherein the at least one different part is determined using dynamic programming matching for the first character strings and the second character strings.
16. A speech processing method executed by a computer, comprising:
selecting a word candidate from among a plurality of word candidates corresponding to input speech data;
determining at least one different part of the selected word candidate corresponding to a difference between the selected word candidate and at least one of the other of the plurality of word candidates; and
outputting speech of the selected word candidate, the speech distinguishing the at least one different part of the selected word candidate from the rest of the selected word candidate.
a terminal device including a first memory and a first processor, the first processor coupled to the first memory and configured to transmit first speech information of an input speech data; and
a server including a second memory and a second processor, the second processor coupled to the second memory and configured to:
receive the first speech information from the terminal device,
extract, based on an input speech data, a plurality of word candidates including a first word candidate and a second word candidate from a memory, the plurality of word candidates being candidates for a word corresponding to the input speech data,
US14/196,202 2013-03-28 2014-03-04 Speech processing method, device and system Abandoned US20140297281A1 (en)
JP2013070682A JP6221301B2 (en) 2013-03-28 2013-03-28 Audio processing apparatus, audio processing system, and audio processing method
JP2013-070682 2013-03-28
US20140297281A1 true US20140297281A1 (en) 2014-10-02
ID=51621695
US14/196,202 Abandoned US20140297281A1 (en) 2013-03-28 2014-03-04 Speech processing method, device and system
US (1) US20140297281A1 (en)
JP (1) JP6221301B2 (en)
US10356245B2 (en) * 2017-07-21 2019-07-16 Toyota Jidosha Kabushiki Kaisha Voice recognition system and voice recognition method
US6477495B1 (en) * 1998-03-02 2002-11-05 Hitachi, Ltd. Speech synthesis system and prosodic control method in the speech synthesis system
US20020184004A1 (en) * 2001-05-10 2002-12-05 Utaha Shizuka Information processing apparatus, information processing method, recording medium, and program
US20100076768A1 (en) * 2007-02-20 2010-03-25 Nec Corporation Speech synthesizing apparatus, method, and program
US20120029909A1 (en) * 2009-02-16 2012-02-02 Kabushiki Kaisha Toshiba Speech processing device, speech processing method, and computer program product for speech processing
JPH10207486A (en) * 1997-01-20 1998-08-07 Nippon Telegr & Teleph Corp <Ntt> Interactive voice recognition method and device executing the method
JP4684583B2 (en) * 2004-07-08 2011-05-18 三菱電機株式会社 Dialogue device
2013-03-28 JP JP2013070682A patent/JP6221301B2/en active Active
2014-03-04 US US14/196,202 patent/US20140297281A1/en not_active Abandoned
US10453074B2 (en) 2016-07-08 2019-10-22 Asapp, Inc. Automatically suggesting resources for responding to a request
US10387888B2 (en) 2016-07-08 2019-08-20 Asapp, Inc. Assisting entities in responding to a request of a user
US10535071B2 (en) 2016-07-08 2020-01-14 Asapp, Inc. Using semantic processing for customer support
US10515104B2 (en) 2018-02-12 2019-12-24 Asapp, Inc. Updating natural language interfaces by processing usage data
JP2014194480A (en) 2014-10-09
JP6221301B2 (en) 2017-11-01
JP2004523004A (en) 2004-07-29 Hierarchical language model
JP4542974B2 (en) 2010-09-15 Speech recognition apparatus, speech recognition method, and speech recognition program
JP4054507B2 (en) 2008-02-27 Voice information processing method and apparatus, and storage medium
US6718303B2 (en) 2004-04-06 Apparatus and method for automatically generating punctuation marks in continuous speech recognition
US7117153B2 (en) 2006-10-03 Method and apparatus for predicting word error rates from text
JP2559998B2 (en) 1996-12-04 Speech recognition apparatus and a label producing method
JP2001100781A (en) 2001-04-13 Method and device for voice processing and recording medium
CN1453766A (en) 2003-11-05 Sound identification method and sound identification apparatus
CN1760972A (en) 2006-04-19 Testing and tuning of speech recognition systems using synthetic inputs
JP4791984B2 (en) 2011-10-12 Apparatus, method and program for processing input voice
JP2003308091A (en) 2003-10-31 Device, method and program for recognizing speech
JP6221301B2 (en) 2017-11-01 Audio processing apparatus, audio processing system, and audio processing method
JP2008134475A (en) 2008-06-12 Technique for recognizing accent of input voice
US20110196678A1 (en) 2011-08-11 Speech recognition apparatus and speech recognition method
US7979270B2 (en) 2011-07-12 Speech recognition apparatus and method
JP5072206B2 (en) 2012-11-14 Hidden conditional random field model for speech classification and speech recognition
TWI455111B (en) 2014-10-01 Methods, computer systems for grapheme-to-phoneme conversion using data, and computer-readable medium related therewith
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:TOGAWA, TARO;SHIODA, CHISATO;OTANI, TAKESHI;SIGNING DATES FROM 20140213 TO 20140221;REEL/FRAME:032369/0713
2017-08-16 STCB Information on status: application discontinuation