JP2010243914A - Acoustic model learning device, voice recognition device, and computer program for acoustic model learning - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an acoustic model learning device for effectively generating an acoustic model for voice recognition and dictation of spoken words of a type where there is a document style text DB having already been shaped. <P>SOLUTION: The acoustic model learning device 78 includes a language model estimating section 188 for estimating a language model 136 of dictation faithful to the actual spoken contents from a language model 186 learned with a document style text (for example, conference minutes) 42 acquired by dictation and shaping of a voice DB (for example, discussion voice corpus) 40 by a human being, a phoneme labeling section 144 for dictation it, attaching a phoneme label to the voice DB 40, and outputting a voice DB 80 with a phoneme label by voice recognition using an initial audio model 130 and the language model 136 of spoken word style dictation estimated by the language model estimating section 188, and an acoustic model learning section for performing the learning of the acoustic model using the voice DB 80 with the phoneme label as learning data. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a speech recognition technology, and more particularly to a speech recognition device that can recognize spoken speech with high accuracy, and an acoustic model learning technology therefor.

  In recent years, the main target of large vocabulary continuous speech recognition has been the speech of carefully spoken words for speech recognition (hereinafter referred to as “reading speech”), as well as spoken speech of lectures and meetings (hereinafter referred to as “spoken speech”). It is moving to.

  Spoken speech accompanies a phenomenon that is not fluent as seen in reading speech. These phenomena include, for example, rephrasing, wording, insertion of utterances called fillers such as “Ah” and “Uh”, lack of particles in Japanese, and lack of pronunciation.

  In general, an acoustic model is required for speech recognition using statistical speech recognition technology. To learn an acoustic model, a speech corpus that is a pair of speech and its faithful transcription must be prepared. In order to increase the accuracy of speech recognition, it is desirable that the size of the speech corpus is large. Usually, such a speech corpus is created manually. However, in the case of spoken speech, because of the phenomenon described above, it is very expensive to create a transcript by hand. Therefore, it is very difficult to construct a large corpus. As a result, a shortage of data for learning the acoustic model necessary for speech recognition becomes a problem.

  In order to cope with this problem, Lamel et al. In Non-Patent Document 1 propose an approach called lightly supervised training (hereinafter referred to as “semi-supervised learning”). This approach creates phoneme labels for learning acoustic models from pre-formatted text data that can be used at low cost instead of faithful transcription of the utterance. In Non-Patent Document 1, it is proposed to give a phoneme label as follows for news speech.

  Many broadcasts are given subtitles. It is conceivable to create a phoneme label using this caption as text data for broadcasting. However, according to Non-Patent Document 1, subtitles contain many errors and cannot be used as phoneme labels as they are. Therefore, in Non-Patent Document 1, a phoneme label for broadcast sound is created by performing speech recognition using a language model learned from subtitle text data. According to Non-Patent Document 1, since there are many non-speech segments such as music and so-called CMs in news speech, the reliability of speech recognition results is not high. Therefore, Non-Patent Document 1 reports that it is effective to match the result again with the caption after the speech recognition and use only the speech recognition result in the matched section.

  Non-Patent Document 2 is also intended for broadcast audio, but in order to cope with expressions that do not appear in subtitles, a language model constructed from subtitles and a separately constructed baseline language model are largely divided into the former. It is synthesized by applying weights, and speech recognition is performed using this language model. Non-Patent Document 2 describes not only normal ML (maximum likelihood) learning but also phoneme error minimum (MPE) learning, which is a type of discriminative learning, by adding learning data using a created phoneme label. Also reported improved recognition accuracy.

L. Ramel et al., “Study on Acoustic Model Learning with Associate Teacher”, ICASSP, Vol. 1, pp. 477-480, 2001 (L. Lamel et al. “Investigating lightly supervised acoustic model training.” In ICASSP, Vol. 1, pp. 477-480, 2001) H. Y. Chang et al., “Improvement of Broadcast News Transcription by Semi-Supervised Discriminative Learning”, IEEE-ICASSP, Vol. 1, pp. 737-740, 2004 (H.Y. Chan et al., “Improving broadcast news transcription by lightly supervised discriminative training.” In IEEE-ICASSP, Vol. 1, pp. 737-740, 2004) P. Morlic et al., “Semi-supervised acoustic model learning for EPPS recordings” INTERSPEECH, pp. 224-227, 2008 (M. Paulik et al., “Lightly supervised acoustic model training on epps recordings” In INTERSPEECH pp. 224-227, 2008) Akita Yuya et al., “Conversion of language model into spoken language style based on the framework of statistical machine translation”, IEICE Technical Report, SP2005-108, NLC2005-75 (SLP-59-19), 2005.

  In recent years, attempts have been made by the National Diet, local councils, etc. to create minutes using speech recognition. This is a social situation in which it is difficult to train stenographers, because there is a need for efficiency and cost savings for the work of public institutions, a decrease in the number of skilled stenographers who have taken minutes. The reason is. Of course, the background is the expansion of necessary hardware and software, such as the spread of high-performance computers and the development of speech recognition technology.

  However, since the question and answer session of the Diet, especially the committee, is a typical spoken language, it is difficult to create a speech corpus as already mentioned. As a result, there is a problem that the accuracy of the acoustic model for spoken speech cannot be increased and the result of speech recognition is not good.

  Considering the reports of Non-Patent Document 1 and Non-Patent Document 2, semi-supervised learning is considered to be an effective technique for speech recognition of spoken words about broadcasting. Since utterances at parliamentary committees and the like are typical spoken words, there is a high possibility that a minutes can be created by performing speech recognition using an acoustic model learned by semi-supervised learning.

  Already, Non-Patent Document 3 reports the creation of conference minutes using semi-supervised learning for European Parliament audio. In Non-Patent Document 3, semi-supervised learning using text from the proceedings of the European Parliament is used to create phoneme labels for speech data. More specifically, a language model is constructed using a conference record created manually, and a phoneme label is created by performing speech recognition corresponding to the conference record using this language model. An acoustic model is constructed using the speech with the phoneme label, and a conference record is created by performing speech recognition of a new conference speech.

  In Non-Patent Document 3, the language learned by using the conference minutes of all conferences uniformly by learning a language model by applying a large weight to the text of a specific conference and recognizing the speech of the conference. It has been reported that phoneme labels with higher accuracy were obtained than when the model was used.

  As reported in Non-Patent Document 3, there is no problem as long as the accuracy when a phoneme label is given by using a conference record itself manually created as a language model is satisfactory. However, as described below, there are problems that need to be solved, especially in preparing minutes of the Japanese Diet and local assembly.

  In the case of the European Parliament, there are many things that correspond to the remarks made at the plenary session of the Japanese Diet, so the remarks are made relatively carefully and the problems peculiar to spoken language do not arise. As a result, in the European Parliament, the difference between the minutes and actual utterances is small, and even if the text data of the minutes is used as it is for the creation of a language model, the accuracy of the phoneme labeling does not decrease so much.

  However, discussions in the Japanese Diet are centered on the committee, not the plenary session. Discussions at the committee are more interactive compared to the plenary session, and are mainly spontaneous. In particular, the questioner at the committee speaks while thinking with a simple memo and taking into account the contents of the answer, so frequent rephrasing, poses, insertion of fillers, etc. occur during utterances. To do. In the case of respondents, there are few such problems compared to the questioner, but there are still more problems specific to spoken language than in the plenary session.

  Currently, proceedings are created by a stenographer. For this reason, the above meaningless speech, rephrasing, pronunciation laziness, etc. are corrected and shaped into an expression close to written language. These tasks are intelligently advanced and are difficult to reproduce on a machine. However, the difference between the actual utterance content and the minutes is so large that it is impossible to use the minutes as they are for adding phoneme labels to the sound data for creating an acoustic model.

  However, if the phoneme labeling is to be performed on the conference voice without using the conference record at all, as described above, it is necessary to newly transcribe the conference speech, which entails a huge cost. Therefore, there is a need for a technology that enables efficient phoneme labeling for a large amount of speech while effectively using an existing conference record. These problems are not limited to conference proceedings, but are common when automating text-to-speech speech data with pre-written transcript text data, such as creating lecture or lecture transcripts at universities and high schools. It is a problem. Furthermore, when referring to a photographed image, for example, in a trial, it may occur that a request to search for a related portion in the image again after documenting the main statement in the image is considered. It is done. Even in such a case, it is convenient if a phoneme label can be efficiently given to the voice.

  In the case of spoken language, the speaker, topic content, surrounding acoustic environment, and the like may change from time to time. For example, when there is a cabinet reshuffle, the ministers who answer in the Diet change. If there is a change of government, the former ruling and opposition parties may reverse, but the utterance style is likely to change as the position changes. In such a case, it is desirable that the acoustic model for creating a transcript can be easily updated so that it can follow changes in the environment. Conventionally, there has been no technology for efficiently assigning phoneme labels to such a large amount of spoken speech data.

  SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide an acoustic model learning device capable of effectively creating an acoustic model for converting text-to-speech speech data in which formatted text data exists. .

  Another object of the present invention is to provide an acoustic model learning apparatus that can easily update an acoustic model for converting spoken speech data in which pre-formed text data exists into text data according to environmental changes. Is to provide.

  The acoustic model learning apparatus according to the first aspect of the present invention is a transcription style transcription that is faithful to the actual content of speech from a language model that is learned from a document style text obtained by transcription and formatting of a speech database. A speech database by speech recognition using a language model estimation means for estimating a language model for a voice, a preliminarily prepared initial acoustic model, and a spoken language transcription language model estimated by the language model estimation means Phoneme labeling means for attaching a transcript and its phoneme label, and acoustic model learning for learning or updating a speech recognition acoustic model using the speech database with the phoneme label attached by the phoneme labeling means as learning data Means.

  In this acoustic model learning apparatus, the language model estimation means estimates a language model for the spoken language style transcription from the language model learned by the document style text. Using this language model and the initial acoustic model, the phoneme labeling means assigns the transcription and the phoneme label to the speech database from which the utterance is based. The acoustic model learning means learns the acoustic model for speech recognition using the speech database to which the phoneme label is assigned as learning data.

  A language model for transcription of spoken language style is estimated from a language model learned from document style text. By using this language model, transcription and phoneme labels are attached to the speech database that is the basis of the utterance style text. Even if there is insertion), speech recognition can be performed with high accuracy and faithful to the speech. Thus, in order to learn the acoustic model for speech recognition using the speech data labeled faithfully to the speech speech as learning data, when performing speech recognition of new speech data using this speech recognition acoustic model Accuracy can be increased.

  Preferably, the language model estimation means includes an N-gram creation means for creating an N-gram language model for each turn from the document style text corresponding to each turn of the speech database, and an N-gram creation means. Means for estimating a spoken N-gram language model of spoken style transcription from each of the generated turn-by-turn N-gram language models. The phoneme labeling means includes a language model selecting means for selecting a corresponding N-gram language model among spoken N-gram language models for each turn of the speech database, and a language for each utterance turn of the speech database. Speech recognition means for performing speech recognition using the N-gram language model selected by the model selection means and the initial acoustic model, and for giving a transcription and a phoneme label for each turn of the speech database. Including.

  The utterance style of the utterance in the voice database varies depending on the speaker and the topic. Create an N-gram for spoken style transcripts for each turn, and perform speech recognition using the spoken N-grams obtained from that turn for each turn. Accuracy can be increased. As a result, the learning efficiency of the speech recognition acoustic model can be increased, and the accuracy of speech recognition using the speech recognition acoustic model can be increased.

  More preferably, the acoustic model learning device is configured to generate a document based on a correspondence corpus created based on a part of spoken language style transcription in the speech database and a part corresponding to the part of the document style text. Further included is a conversion model learning means for learning a conversion model that statistically shows the conversion from the expression in the style text to the expression of the spoken style transcription. The language model estimation means includes means for estimating an N-gram language model of spoken style transcription by applying a transformation model to each N-gram language model for each turn.

  When a correspondence corpus is created from a part of spoken language style transcription in the speech database and a corresponding part of the document style text, the conversion model learning means learns a conversion model from the correspondence corpus. This conversion model shows statistically the conversion from the expression in the document style text to the expression in the spoken style transcription. The language model estimation means applies the conversion model to each of the N-gram language models for each turn, and creates an N-gram language model of the spoken language style transcription.

  It is assumed that the association corpus itself is created manually. However, using the language model obtained in this way, phoneme labeling of not only the part of the speech database from which the correspondence corpus was created, but also a larger speech database including that part of the speech database. Can be performed automatically. Compared with the case where the corpus is created for the entire speech database, phoneme labeling of a large number of speech databases can be performed with high accuracy and efficiency with less effort.

  More preferably, the audio database is a deliberation audio corpus that includes audio of some deliberation, and the document style text is a minutes of the deliberation.

  Speeches deliberated by the Diet, etc., frequently exhibit spoken language-specific phenomena (fillers, grudges, etc.) and are present in large quantities. Therefore, it is difficult to manually perform phoneme labeling of the speech database. However, there are complete minutes of the discussions in a document style. Therefore, it is possible to recognize the speech of the discussion efficiently and accurately by learning the acoustic model for speech recognition as described above using the minutes as document style text and the speech database as the speech database. become.

  A speech recognition device according to a second aspect of the present invention is an acoustic model for storing a speech recognition acoustic model learned by any of the acoustic model learning devices using a predetermined speech corpus as learning data. And a speech recognition means for performing speech recognition on the input utterance data using the storage means, the speech recognition acoustic model stored in the acoustic model storage means, and the speech recognition language model.

  A computer program according to a third aspect of the present invention provides a spoken language style writing that is faithful to the actual content of speech from a language model obtained by learning a computer from a document style text obtained by writing and formatting a speech database. A speech database that uses a language model estimator for estimating the language model of transcription, an initial acoustic model prepared in advance, and a spoken language style transcript language model estimated by the language model estimator is used to create a speech database. A phoneme labeling means for attaching a transcription and its phoneme label, and an acoustic model learning means for learning or updating a speech recognition acoustic model using a speech database with a phoneme label attached by the phoneme labeling means as learning data To function as.

1 is a block diagram of a conference record creating system 30 according to a first embodiment of the present invention. It is a figure which shows typically the correspondence of the discussion audio corpus 40 shown in FIG. It is a block diagram of the phoneme labeling process part 78 shown in FIG. It is a schematic diagram which shows a part of content of the matching corpus used by embodiment of this invention. It is a flowchart which shows the control structure of the computer program which implement | achieves the process part which learns the conversion model of spoken language / written language. It is a flowchart which shows the control structure of the computer program which implement | achieves the process part which produces the N-gram for every turn, and the process part which converts the written word of N-gram into a spoken word. It is a figure which shows typically the relationship of the computer which comprises the meeting minutes production system which concerns on 1st Embodiment. FIG. 8 is an external view of a computer for creating an acoustic model in the conference record creating system shown in FIG. 7. It is a block diagram which shows the hardware constitutions of the computer shown in FIG. FIG. 8 is an external view of a computer used for creating a conference record in the conference record creating system shown in FIG. 7.

  In the following description, the same parts are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated. In the embodiments described below, unigrams, bigrams, and trigrams are used as N-grams.

[Principle of Embodiment]
In the present embodiment, an automatic transcription system (conference record creation system) of the Diet deliberation voice is constructed based on the following concept. In the Japanese parliament, as mentioned above, unlike the European Parliament, discussions are mainly held by the committee. For this reason, it is mainly interactive and spontaneous utterances rather than the European Parliament deliberation. Such utterances include many fillers, stagnations, repetitions, and so on. In the minutes created manually, such fluent utterances are “translated” into fluent utterances. That is, in Japan, the difference between the actual utterance content and the minutes is large. Therefore, the process of creating a phoneme label based on the minutes is difficult as it is, and it becomes a problem whether to appropriately deal with a phenomenon specific to spoken language as follows.

  FIG. 2 shows an example of actual utterances and minutes of proceedings in the Diet discussion sound.

  In FIG. 2, a discussion voice corpus 40 composed of actual utterances and a corresponding conference record 42 are shown in comparison. The deliberation voice corpus 40 includes, for example, the sound of the deliberation of the Diet, and constitutes a voice database. The utterance 100 corresponds to the conference record 110, the utterance 102 and the conference record 112, and the utterance 104 and the conference record 114, respectively.

  As can be seen from FIG. 2, in the minutes, the particle “ga” is inserted and the fillers such as “i”, “e”, and “ano” are removed. In other words, conversion from spoken language to written language is performed.

  A framework for constructing a statistical model for style conversion of a language model from a corpus that correlates such spoken language (faithful transcription of the content of the statement) and a formatted document (meeting minutes) is a non-patent document. 4 proposed. In the embodiment described below, the statistical language model conversion is applied to individual minutes, thereby constructing a spoken language language model from the written language model and performing speech recognition using the language model. To create phoneme labels for spoken words.

  In the statistical style conversion of the language model, the spoken language style V and the document style W are converted based on the framework of statistical machine translation. This conversion is bidirectional. In other words, the conversion model can be applied in the direction of shaping from the transcription of the spoken language to the document style and in the direction of restoring the transcription from the text in the document style.

  Decoding is performed based on the following Bayesian rule according to the framework of statistical machine translation.

Where p (W) is the document style N-gram probability, p (V) is the N-gram probability of spoken style text V, and p (W | V) is the document style text for spoken style text V. The conditional probability of W, p (V | W), indicates the conditional probability of spoken style text V with respect to document style text W, respectively. The denominator of each expression is usually ignored.

  What is important here is that it is much more difficult to uniquely determine the spoken-style text V by the equation (2) than the process of shaping by the equation (1) because the text V can be varied. For example, fillers may be inserted randomly in equation (2) (ie, the form of spoken-style text V that includes fillers may vary), but fillers are removed with probability 1 in equation (1) ( That is, the filler in the spoken-style text V is surely removed upon conversion to the document-style text W. Therefore, it is more meaningful to estimate the statistical language model of the spoken-style text V as in the following equation (3) than to uniquely restore the spoken-style text V.

  The important point is that the document-style text W exists more abundantly than the text V which is a transcription of the spoken language. That is, according to Equation (3), it is possible to robustly estimate the language model p (V) for spoken speech recognition using abundant document style text.

  The actual conversion is done by manipulating the N-gram count as follows:

  v and w are conversion patterns in each style. According to the equation (4), the replacement w → v, the omission of w, and the insertion of v can be modeled in consideration of the context. The conditional probabilities p (v | w) and p (w | v) are statistically estimated from the corpus of correspondence between the transcription and the document style text. More specifically, these conditional probabilities conditional probabilities p (v | w) and p (w | v) are estimated from the number of appearances of each pattern in the corpus.

Consider adjacent words in the pattern to be an appropriate model. For example, the filler “Ah” is modeled as {w = (w ₋₁ , w ₊₁ ) → v = (w ₋₁ , ah, w ₊₁ )}. When smoothing using the part of speech information is performed, it is possible to cope with the sparseness of the data.

[First Embodiment]
Referring to FIG. 1, a conference record creation system 30 according to the first embodiment of the present invention is generally a speech recognition system, and includes a deliberation audio corpus 40 and a conference corresponding to the deliberation audio corpus 40. This is for outputting a transcript 56 by recognizing the discussion voice 54 from the record 42. In this embodiment, the statistical style conversion (written language → spoken language) of the language model described above is applied to semi-supervised learning of an acoustic model. The National Diet has created a large-scale archive of recorded audio data. These voices are not given transcription by hand, but pre-formatted conference minutes can be used. Therefore, if a phoneme label can be automatically created based on a conference record, abundant speech data can be used as it is as learning data for an acoustic model.

  Referring to FIG. 1, for this purpose, in the minutes recording system 30, a faithful transcript 70 created from a partial corpus 68 that is a part of the deliberation audio corpus 40 and a partial corpus of the minutes 42 are recorded. First, a correspondence corpus 76 is created from the partial conference records 72 corresponding to 68 by manual corpus creation processing 74. The partial corpus 68 and the partial conference record 72 are associated with each other. That is, the characters and symbols constituting the text data of the partial conference record 72 are assigned in advance to the voice included in the partial corpus 68. The transcription 70 can give a phoneme label to the partial corpus 68.

  A conference record is created for each conference such as a budget committee, a legal committee, etc., and a speaker ID in the conference is assigned to each utterance, and a text for each turn can be extracted accordingly. Each meeting is approximately 2 to 5 hours long, and each turn is approximately 10 seconds to 3 minutes long (average 1 minute). Here, “turn” refers to a group of utterances spoken by a speaker. For example, a series of utterances are divided into a plurality of turns, such as one turn when the questioner makes a question, one answer by the answerer and the next turn after answering the question. Even consecutive utterances by the same speaker are considered different turns if the topic is different. Each of the utterances 100, 102, and 104 shown in FIG. 2 is one turn. Corresponding conference records 110, 112 and 114 can also be read for each turn.

  In this embodiment, an N-gram used as a language model in speech recognition for providing a phoneme label is not an entire conference including many speakers or topics, but an individual conference so as to be a stronger constraint. Create an N-gram every turn. In the method according to this embodiment, since the size of each N-gram does not increase, it is possible to prepare an N-gram for each detailed unit such as a turn. In addition, there is an advantage that the possibility that extra expressions are mixed is extremely low as in the case where the baseline language model is used for speech recognition.

  The association corpus creation process 74 is a process of creating each transcription 70 of the partial corpus 68 and then associating each transcription word with a word of the partial conference record 72. This process is manual. However, the association corpus 76 corresponds to only a part of the deliberation voice corpus 40 (partial corpus 68) and a part of the conference record 42 (partial conference record 72). Therefore, the amount of work for creating the association corpus 76 may be much smaller than when the entire discussion speech corpus 40 is written up.

  In the present embodiment, since N-grams are used as language models, it is necessary to pay attention to the handling of poses when creating the association corpus 76. This is because even if pauses are inserted in the audio data, the pauses are not inserted as they are in the minutes but are often inserted in the form of punctuation marks. Although there are various ways of handling poses, in this embodiment, “,” is handled as a short pause (<sp>), and “.” Is treated as a silent section (<sil>). In creating the association corpus 76, the pose marks are unified in this way.

  The conference record creation system 30 uses the correspondence corpus 76 created in this way, and uses the correspondence corpus 76 to estimate a conversion model 122 for converting a language model for written language into a language model for spoken language according to Expression (4). A written language conversion model learning unit 120, a speech recognition acoustic model learning unit 44 for learning the acoustic model 48 corresponding to speech recognition of spoken words from the deliberation speech corpus 40 using the conversion model 122, and a conference record The language model learning unit 46 for learning the statistical language model 58 for speech recognition from the whole 42 and the conversion model 122 are used to use the language model 58 for written language learned from the conference record 42 for the spoken language. A language model conversion unit 60 for converting to the language model 50, and an acoustic model 48 each adapted for spoken language, and Using the word model 50, and a speech recognition device 52 for outputting as a cause 56 write the recognition result by recognizing speech deliberations audio 54.

  Specifically, the spoken / written language conversion model learning unit 120 examines how the corresponding portion in the transcript 70 has changed for each N-gram appearing in the partial conference record 72, and the result thereof. Count. For example, w = “<sp> this bill” (<sp> represents a short pause) appears 500 times in the partial conference record 72, and 50 times of the transcript 70 are v = “<sp> "(Filler" E "" is inserted), p (v | w) = 50/500. By counting such counts for all N-grams and their variations, a conversion model 122 according to equation (4) is obtained. What is obtained by this tabulation is a count indicating what kind of change has occurred and how many times. This value can be equated with a probability indicating how often the expression of the document style changes to what expression of the spoken language style.

  The acoustic model learning unit 44 for speech recognition performs a process of attaching a phoneme label to the speech of the deliberation speech corpus 40 by speech recognition using the deliberation speech corpus 40, the phoneme-labeled partial corpus 68, and the conversion model 122. A phoneme labeling processing unit 78 for outputting the labeled speech database 80 and an acoustic model learning unit 82 for learning the acoustic model 48 for spoken language by a normal learning method using the phoneme labeled speech database 80 as learning data. Including.

  Referring to FIG. 3, the phoneme labeling processing unit 78 includes an initial acoustic model learning unit 130 for learning the initial acoustic model 132 from the phoneme-labeled partial corpus 68, and the conference record 42 for each turn of the conference record 42. By creating N-gram statistical data from text data, an N-gram creation unit 184 for each turn for creating an N-gram 186 for each turn, and the N-grams included in each N-gram 186 for each turn An N-gram conversion unit 188 for outputting the spoken-language N-gram 136 by performing the conversion expressed by the equation (4), which is determined by the conversion model 122, on the probability.

  The N-gram creation unit 184 for each turn extracts N-gram entries from the text of the minutes of each turn and counts the number of appearances. This results in an N-gram 186 for each turn. For each entry in the N-gram 186 per turn, a spoken N-gram 136 is obtained for each turn by applying the transformation model 122.

  The phoneme labeling processing unit 78 further selects each turn in the discussion speech corpus 40 in turn, and outputs a turn / speech selection unit 138 for outputting information for identifying the turn and the sound of the selected turn, An N-gram selection unit 140 for receiving information indicating the turn selected by the voice selection unit 138 and selecting the N-gram 142 corresponding to the turn from the spoken N-gram 136, and an initial acoustic model 132 and N-gram 142, in particular, N-gram 142 is used as a language model, speech recognition of the speech output from turn / speech selector 138 is performed, and the word level and phoneme level are recognized in the speech. A speech recognition device 144 for attaching the result and outputting it to the phoneme-labeled speech database 80.

  An existing statistical speech recognition device can be used as the speech recognition device 144. Here, what outputs the recognition result of a word level and a phoneme level is used, However, You may output only the result of a phoneme level. The voice recognition device 144 outputs voice data to which a recognition result is attached in a form of being divided into short utterance sections of about 30 seconds at the maximum according to a pause during utterance. Subsequent learning is performed in units of this interval.

  Each phoneme label in the speech database with phoneme label 80 obtained in this way is determined in consideration of the appearance probability of a phoneme string that appears in spoken language but does not appear in the document style. In addition, since the N-gram learned only for that turn is used for each turn, the accuracy of speech recognition, that is, the accuracy of the phoneme label to be applied is increased. In addition, even when a large amount of speech exists in the deliberation speech corpus 40, phoneme labels can be automatically assigned to all of them.

  Therefore, when the acoustic model 48 is created by the acoustic model learning unit 82 shown in FIG. 1 from the phoneme-labeled speech database 80 by a normal method, it can be sufficiently expected that the accuracy of the recognition result by the speech recognition device 52 is increased. .

  On the other hand, the language model 50 used by the speech recognition device 52 is also obtained by applying the conversion model 122 to N-grams appearing in the conference record 42, and the occurrence probability of phoneme strings peculiar to spoken language is high. It has been included.

  As described above, since the acoustic model 48 and the language model 50 obtained in consideration of the occurrence probability of the phoneme string peculiar to the spoken language are used, the speech recognition device 52 causes an event that frequently occurs in the spoken language, that is, filler insertion, Regardless of the grudge and the lack of pronunciation, it is possible to output a highly accurate transcript of the deliberation speech corpus 40.

  FIG. 4 shows two sentence examples in the association corpus 76. In FIG. 4, the speech that has been uttered in the discussion speech corpus 40 but has been deleted in the conference record 42 is enclosed in curly braces {} like “{e}” at the beginning of the utterance 160 in FIG. It is shown. Voices that are not uttered in the discussion speech corpus 40 but are inserted in the minutes 42 are enclosed in parentheses () as shown in the utterance 162 in FIG. It is shown by. The part in which the expression of the utterance of the deliberation voice corpus 40 is changed to another expression in the minutes 42 is surrounded by curly braces like “{de / no}” in the utterance 160, and the deliberation voice corpus The expression in 40 is shown before “/”, and the expression in the minutes 42 is shown after “/”.

  This association corpus can be considered as a parallel corpus for creating a translation model when the transcript 70 and the partial conference record 72 are considered to be in different languages. Usually, in the translation model, in addition to inserting, deleting, and replacing words, editing such as changing the order is considered. However, since the language itself has the same limit, changing the order is not considered.

[Program structure of spoken / written language conversion model learning unit 120]
Referring to FIG. 5, the computer program that realizes the learning process of conversion model 122 by spoken / written language conversion model learning unit 120 starts the execution of the program in response to an instruction to start the process from the user, and the storage area Includes step 190 for performing initial setting such as securing the variable, clearing the variable, step 192 for opening the file of the corpus corpus 76, and step 194 for assigning 0 to the repetition variable i.

  The repetition variable i is a variable indicating the position of the word to be processed in the association corpus 76 and increases by 1 from 0. Hereinafter, the word at the position indicated by the variable i is written as “word (i)”.

  The program further determines whether or not the value of the variable i is larger than the number of all words in the corpus corpus 76, and branches the flow of control according to the determination result, and the determination result of step 196 Is executed when NO is set, and in the association corpus 76, a step 198 for adding one each to the count of the unigram, bigram, and trigram beginning with the word (i) of the partial conference record 72, and the variable i And step 200 for returning 1 to 196 by adding 1 to. By executing the processing from step 196 to step 200 for all the words in the association corpus 76, an N-gram model of the partial conference record 72 is created.

  This program is further executed when the determination result in step 196 is YES, and step 202 for resetting the reading position of the association corpus 76 to the top, and the unigram calculated in the partial conference record 72 following step 202. Step 204 for calculating the conversion model 122 by counting how the transcription 70 changes for each of the bigram and trigram, and the conversion model 122 calculated in step 204 is output as a file. And step 206 for ending the processing.

[Program structure of N-gram creation unit 184 and N-gram conversion unit 188 for each turn]
Referring to FIG. 6, the computer program for realizing N-gram creation unit 184 and N-gram conversion unit 188 for each turn is used for initial setting such as securing and initializing necessary storage areas as the program starts. Whether or not the processing of all the turns has been completed by comparing the repetition variable i with the number of turns included in the partial conference record 72 to be processed. And step 214 of branching the control flow according to the determination result.

  This program is further executed when the determination result in step 214 is NO, and the step 216 for reading the minutes of the turn (i) from the partial minutes 72 and the minutes of the turn (i) read in the step 216 are read. The N-gram is generated and output to a predetermined storage medium. Step 218 is followed by step 220 where step 1218 adds 1 to the value of iteration variable i and returns control to step 214.

  This program is further executed when the determination result at step 214 is YES, step 222 for reading the conversion model 122 from the external storage medium to the main storage device, step 224 for substituting 0 for the repetition variable i, and the repetition variable. Whether or not N-gram conversion (document style → spoken style conversion) has been performed on the minutes of all the minutes in the partial minutes 72 by comparing the value of i with the number of turns included in the minutes. In step 226 for branching the control flow according to the determination result, and in step 226, it is determined that the conversion of the N-gram for the minutes in the partial minutes 72 has not been completed. It is executed in response, and for all N-grams of turn (i), the transformation model 122 is applied to verify the spoken language style. It includes the step 230 to recalculate and update the estimated value, and a step 232 to return the sum to control one to repeat the variable i in step 226.

[Realization by computer system]
The conference record creation system 30 whose structure has been described above is substantially realized by a computer. It is also possible to mount the entire meeting record creation system 30 on a single computer. However, while the acoustic model 48 and the language model 50 are learned by using a large amount of the discussion voice corpus 40 and the minutes 42, the discussion voice corpus 40 and the minutes 42 are not necessary for creating the minutes. . Therefore, it is more convenient for maintenance to separate the two. In addition, learning of the conversion model and learning of the acoustic model have a great influence on the performance of the system, so it is preferable that the conversion model learning and the acoustic model learning be performed by the system administrator or the system administrator.

  Therefore, as shown in FIG. 7, the conference record creating system 30 according to the present embodiment performs learning by the learning computer system 250 for learning the acoustic model 48 and the language model conversion unit 60, and the computer system 250. A computer system 300 for creating a conference record that performs a process of using the acoustic model 48 and the language model 50 to recognize a discussion voice and output a transcript. As will be readily understood by those skilled in the art, if a plurality of computer systems 300 for creating minutes are used, minutes of discussions in a plurality of committees can be created using a common acoustic model 48 and language model 50. it can.

  Referring to FIG. 8, a learning computer system 250 includes a computer 260, a monitor 262, a keyboard 266, a mouse 268, a microphone 290, and a pair of speakers 258 that are all connected to the computer 260. The computer 260 is provided with a DVD drive 270 capable of reproducing and recording a DVD (Digital Versatile Disc), and a memory port 272 into which a semiconductor memory storage device according to a predetermined standard can be mounted. The internal configuration of the computer 260 will be described later with reference to FIG.

  Referring to FIG. 9, in addition to DVD drive 270 and memory port 272 shown in FIG. 8, computer 260 has a CPU (Central Processing Unit) 276 and a bus 286 connected to CPU 276, both connected to bus 286. ROM (Read Only Memory) 278, RAM (Random Access Memory) 280, large capacity hard disk 274, network interface 296, and sound board 288.

  A DVD 282 is attached to the DVD drive 270. A semiconductor memory 284 is attached to the memory port 272. The CPU 276 accesses the DVD 282 and the semiconductor memory 284 via the bus 286, the DVD drive 270, and the memory port 272, respectively, and can read and write data.

  The keyboard 266, the mouse 268, and the monitor 262 are all connected to the bus 286 of the computer 260 via an interface (not shown). The speaker 258 and the microphone 290 are connected to the sound board 288.

  The discussion speech corpus 40, conference record 42, partial corpus 68, transcript 70, partial conference record 72, association corpus 76, conversion model 122, phoneme-labeled speech database 80, acoustic model 48, language model 50 in the above embodiment. And 58 can be realized by any of the RAM 280, the large-capacity hard disk 274, the DVD 282, and the semiconductor memory 284. Actually, the most efficient storage device is selected to realize each storage unit depending on the capacity of data to be stored, the speed required for reading and writing, and the like. In the present embodiment, these are stored in the large-capacity hard disk 274 and loaded into the RAM 280 when used.

  Referring to FIG. 10, a conference record creation computer system 300 used in the conference record creation system 30 according to the present embodiment includes a computer 310, a monitor 320, a keyboard 322, and a mouse, all connected to the computer 310. 324, a microphone 328, and a pair of speakers 326. Although not shown, the computer 310 is provided with a headphone connection terminal, so that sound can be reproduced by the headphone. The computer 310 is preinstalled with a speech recognition program for realizing the speech recognition apparatus 52 shown in FIG. 1 and an editing program for editing the proceedings file output by the speech recognition program. Furthermore, the computer 310 has a large-capacity HDD, and can store the acoustic model 48 and the language model 50 received from the computer system 250 via the network in this HDD.

  The hardware configuration of the conference record creating computer system 300 is the same as that shown in FIG. Therefore, details thereof will not be repeated here.

[Operation]
The conference record creation system 30 described above operates as follows. The operation of the conference record creation system 30 is divided into several phases. Hereinafter, these phases will be described in order.

-Creation of the corpus 76-
With reference to FIG. 1, first, an association corpus 76 is created in the computer system 250 from the existing discussion voice corpus 40 and the conference record 42. By manual operation, the partial corpus 68 is extracted from the discussion voice corpus 40, and the corresponding partial meeting record 72 is extracted from the meeting record 42. The partial corpus 68 is reproduced, and a faithful transcription 70 of the deliberation voice is manually created for each turn. From the transcription 70 and the partial conference record 72 created in this way, the association corpus creation process 74 is also performed manually, and the association corpus 76 is created.

  Here, the transcription corpus 76 is created after the transcription 70 is created once. However, even if the corpus 76 is created by directly editing the partial meeting record 72 on the screen while reproducing the partial corpus 68. Good.

  The completed corpus 76 is stored in the large-capacity hard disk 274.

-Creation of transformation model 122-
The correspondence corpus 76 is a pair of a faithful transcript of the spoken-style partial corpus 68 and a formatted (document-style) partial conference record 72, which is shown in FIG. 4 in the present embodiment. It is like this format. The spoken / written language conversion model learning unit 120 creates a normal N-gram for the portion of the partial conference record 72 in the association corpus 76 (FIG. 5, Steps 196-200). Further, the spoken / written language conversion model learning unit 120 examines the corresponding portion in the transcript 70 for each entry of the N-gram, and counts the number of each change if any, and at the time of counting all of them. The conversion model 122 is obtained by calculating the ratio for each variation for each entry (step 204).

  This process is performed as follows, for example. N-gram trigram w = “<sp> this bill” appeared 500 times in the partial meeting record 72, and 50 of those in transcript 70 were v = “<sp> eh this bill”. To do. In this case, p (v | w) = 50/500. The spoken / written language conversion model learning unit 120 counts the number of occurrences of v (50 in the above case). If there is no other modification of trigram w = “<sp> this bill”, if there is a document style trigram w = “<sp> this bill” in total of 500, the corresponding spoken style expression The number of occurrences is 50 for "<sp> E- this bill" and 450 (= 500-50) for "<sp> this bill".

  In this way, the spoken / written language conversion model learning unit 120 counts the number of occurrences in the transcript 70 for each modification of each N-gram entry obtained from the association corpus 76. Based on the counting result, the conversion coefficient of equation (4) is calculated for each N-gram of the spoken language style that appears in the transcript 70. As a result, a conversion model 122 is obtained. The obtained conversion model 122 is output and stored in the HDD (FIG. 5, step 206).

-Phoneme labeling process of the deliberation speech corpus 40-
When the conversion model 122 is obtained as described above, a phoneme label can be assigned to the deliberation speech corpus 40 as follows.

  First, as shown in FIG. 3, the initial acoustic model 132 is learned by a normal method by the initial acoustic model learning unit 130 using the partial corpus 68 and the partial conference record 72. Next, for each turn of the conference record 42, an N-gram 186 (see FIG. 3) for each turn is obtained by the N-gram creation unit 184 for each turn (FIG. 6, steps 214-220). The N-gram conversion unit 188 applies the conversion model 122 to the obtained N-gram 186 for each turn, so that a spoken N-gram 136 is obtained for each turn.

  The turn / speech selection unit 138 sequentially selects each turn of the deliberation speech corpus 40 and provides turn information to the N-gram selection unit 140. The N-gram selection unit 140 selects the spoken N-gram obtained from the selected turn from the spoken N-gram 136 according to the given turn information, and performs speech recognition as the N-gram 142. To device 144. On the other hand, the turn / voice selection unit 138 provides the voice recognition device 144 with the voice data of the selected turn.

  The speech recognition apparatus 144 uses the N-gram 142 as a language model, performs speech recognition on the speech selected from the deliberation speech corpus 40 using the initial acoustic model 132, and uses the speech recognition result as a phoneme label for the deliberation speech corpus. It is given to 40 audio data. In the speech recognition by the speech recognition apparatus 144, the N-gram 142 converted for spoken language obtained from the turn is used as a language model for each turn. Therefore, for each turn of the deliberation voice corpus 40, a voice recognition result faithful to the voice when spoken is obtained. That is, the phoneme-labeled speech database 80 to which phoneme labels are assigned by the phoneme labeling processing unit 78 is a speech corpus having high-precision phoneme labels that is faithful to the pronunciation of spoken words. In addition, phoneme labels can be automatically assigned to all voices included in the deliberation voice corpus 40 in this way.

-Learning of acoustic model 48-
The phoneme-labeled speech database 80 obtained as described above is a speech corpus to which phoneme labels faithful to spoken words are given. Therefore, by performing learning using the phoneme-labeled speech database 80, an acoustic model 48 suitable for speech recognition of spoken words can be obtained. Since the phoneme-labeled speech database 80 has phoneme labels faithful to spoken words, the acoustic model learning unit 82 only needs to learn a normal acoustic model.

-Learning language model 50-
Apart from the learning of the acoustic model 48, the learning of the language model 50 can also be performed as follows. The language model learning unit 46 uses a normal language model learning method and learns the language model 58 using the minutes 42 as learning data. In this embodiment, unigrams, bigrams, and trigrams are used as language models.

  The language model conversion unit 60 further performs conversion into the language model 50 corresponding to the spoken language by applying the conversion model 122 to each N-gram in the language model 58. In the language model 50 after conversion, a part of the occurrence probability of the document-style N-gram is allocated to the occurrence probability of the N-gram specific to the spoken language, and the occurrence probability of the document-style N-gram is discounted accordingly. Has been.

-Create a new transcript-
The acoustic model 48 and the language model 50 obtained by the computer system 250 in this way are transmitted to the conference record creation computer system 300 and stored in the conference record creation computer system 300. The speech recognition device 52 of the computer system 300 for creating the conference record recognizes the newly recorded discussion speech 54 using the acoustic model 48 and the language model 50, and outputs the speech recognition result as a new transcript 56. To do.

  When learning the acoustic model 48, the entire deliberation speech corpus 40 can be used as learning data. Therefore, the acoustic model 48 can cover various spoken language expressions. Furthermore, in the language model 50, occurrence probabilities corresponding to the comparison results of the transcript 70 and the partial conference record 72 are assigned to expressions unique to the spoken language. Therefore, compared with the case where the language model 58 of only the document style is used, it is possible to improve the accuracy of speech recognition of the spoken language style utterance.

  As described above, according to the conference record creating system 30 according to this embodiment, the transcript 70 is created from the partial corpus 68 that is a part of the deliberation voice corpus 40 and combined with the corresponding partial conference record 72. If the processing for creating the association corpus 76 is performed, the phoneme label can be automatically assigned to the deliberation speech corpus 40, the acoustic model 48 can be learned, and the language model 50 can be learned. For example, even when there is a change of government and the situation of the deliberative voice changes significantly, if the process of creating the corpus 76 is performed manually, the acoustic model 48 and language are automatically processed thereafter. The model 50 can be reconstructed. As a result, an accurate transcript can be created by the voice recognition device 52 even with the discussion voice 54 obtained in a new situation.

  The computer program for realizing the conference record creation system 30 according to the above-described embodiment may be a single program or a combination of a plurality of programs. However, when the conference record creation system 30 is divided and realized by two computer systems as in the above-described embodiment, the programs need to be separated. Among the functions of the above-described units, N-gram creation performed in the spoken / written language conversion model learning unit 120 shown in FIG. 1, language model creation performed in the language model learning unit 46, initial acoustic model learning unit 130, and acoustic model learning For individual functions such as the learning process of the acoustic model executed by the unit 82, a program that has already been widely distributed can be used as it is. Of course, since these programs are created for general use, it is necessary to make appropriate adjustments, but for those who have ordinary knowledge in this technical field, they are within the range that can be easily realized according to the purpose. stay.

  These programs are stored in a storage medium such as a DVD 282, or distributed through a network such as the Internet 252, and are usually stored in a nonvolatile external storage device such as a large-capacity hard disk 274. At the time of execution, the CPU 276 executes an instruction that is copied from the large-capacity hard disk 274 to the RAM 280 and is read from an address indicated by a register called a program counter (not shown) in the CPU 276, thereby realizing the intended function. Since the operation form of the computer hardware itself is well known, no further detailed description will be given here.

[Evaluation experiment]
-Experimental conditions-
The performance of the conference record creation system constructed according to the concept of the above-described embodiment was evaluated using the speech of the House of Representatives.

  Baseline acoustic models and statistical transformation models were learned using 2003 and 2004 data. These data are manually transcribed, and are associated with the minutes in advance. The size of the audio data is 134 hours, and the text size of the proceedings is 1.8M words.

  The acoustic feature amount at the time of speech recognition is a total of 38 dimensions of 12-dimensional MFCC (Mel-Frequency Cepstrum Coefficient), ΔMFCC, ΔΔMFCC, Δ power, and ΔΔ power.

-Phoneme label creation experiment-
A phoneme label creation experiment was conducted on the speeches of the House of Representatives deliberations in 2006 and 2007. The number of conferences is 26, the number of turns is 5,170, and the amount of data is 91 hours. The acoustic model is an HMM (Hidden Markov Model) baseline model trained using 2003 and 2004 data (134 hours). The number of HMM states is 3000, the number of mixtures is 16, and MPE learning has been completed. CMN (Cepstral Mean Normalization) and CVN (Cepstal Variance Normalization) were applied to the feature amount. Speech recognition is performed using Julius (http://julius.sourceforge.jp/), but the search parameters are set lightly assuming that a large amount of data is processed (about twice the real time). Allow time).

  For comparison, phoneme label making experiments were conducted using the following various models. As a language model unit, the conditions for creating one model for the entire meeting were compared with the conditions for creating individual models for each turn. As a method, in addition to the method according to the present embodiment (referred to as “meeting minutes, spoken language conversion”), a spoken language baseline model (“baseline”), a model created only from the minutes (“meeting”) ), A biased LM ("biased LM") synthesized with 100 times the weight of the minutes, and a model ("minutes, filler") with only the filler entry added to the pose position of the minutes model Each was used. The baseline model was created by applying spoken language conversion to seven minutes of conferences from 1999 to 2005.

  Table 1 shows the accuracy of phoneme labels obtained by speech recognition. In Table 1, Corr. (Word correct answer rate) and Acc. (Word recognition accuracy) is a value calculated with a human transcription as a correct answer.

  Referring to Table 1, in the conference unit condition, when the spoken language style is dealt with by the biased LM and the method of the above embodiment, the word recognition accuracy higher than the model of the conference minutes alone was obtained. However, for the 26 conferences, the model in the above embodiment could be constructed in a compact size (100 MB), whereas the biased LM required a very large size (1.6 GB). Therefore, it is considered impractical to apply biased LM to turn-by-turn processing.

  On a turn-by-turn basis, the overall accuracy was higher than on a conference-by-conference basis. In the method according to the present embodiment, the recognition accuracy is 8.6 points higher than when only the minutes are used. The model (Meeting Record, Filler) with a filler added to the word model obtained from the minutes is a simple language model for spoken language, and it supports only the insertion of fillers in the spoken language and considers the context. Corresponds to the case of not. In the method according to the present embodiment, the “meeting record, filler” model exceeds the recognition accuracy by 5.9 points. It turns out that the language model for spoken language can be appropriately estimated from the minutes by the statistical transformation model. According to the method of the present embodiment, 92.1% accuracy and 94.0% word correct rate are realized.

  An example of a phoneme label created according to this embodiment is shown below.

  In this example, the correct phoneme label was obtained by the method according to the present embodiment for the omission of the particle “ga” and the insertion of fillers such as “i”. The insertion of the particle “ni” was incorrect, but this pattern did not exist in the conversion rule in the first place, so it is thought that it could not be predicted by the language model.

-Speech recognition experiment-
Learning data was added using the phoneme label created by the method according to the above embodiment, and the acoustic model was learned using this learning data. The following speech recognition experiment was conducted using the learned acoustic model.

  The baseline model is an acoustic model in which learning is performed by using hand-transcribed phoneme labels using data of 2003 and 2004 (134 hours). The additional data is 91 hours of 2006 and 2007 when the phoneme labels were assigned in the above “phoneme label creation experiment”. For comparison, evaluation is also performed when learning is performed on the same data using a manual phoneme label. Learning is performed according to two criteria, ML (maximum likelihood criterion) and MPE (Minimum Phone Error) criteria. The number of HMM states is 5000, and the number of mixtures is 32. CMN, CVN, and VTLN (Vocal Tract Length Normalization) were applied to the feature amount. The test sets are the House of Representatives Budget Committee on February 26 and 29, 2008 (2.4 hours, 121 turns) and the House of Representatives Budget Committee on October 7, 2008 (3.9 hours, 211 turns).

  Table 3 shows the word recognition accuracy obtained in this experiment.

  Referring to Table 3, in the case of ML learning, using the method according to the present embodiment for any test set, a higher accuracy than the baseline is obtained, and in the case of manual phoneme labeling It can be seen that the level is almost unchanged. Also in the case of MPE learning, the accuracy is improved from the baseline, and in this case as well, the level is almost the same as the manual phoneme labeling.

  As described above, according to the present invention, an acoustic model can be constructed and updated at low cost by semi-supervised learning using statistical spoken language conversion. Therefore, even if data is added to or replaced with the speech corpus for learning the acoustic model, the acoustic model can be reconstructed easily and at low cost. As a result, it is possible to easily cope with speaker changes due to cabinet remodeling or general elections, and changes in the way each speaker speaks.

  The above embodiment relates to a system that automatically creates a committee proceedings record of the Diet. However, the present invention is not limited to such an embodiment. For example, this system can be applied to the creation of subtitles for broadcast programs and university lectures.

  In the above embodiment, the acoustic model 48 and the language model 50 are learned by the computer system 250, and the conference record creation computer system 300 receives the acoustic model 48 and the language model 50 and only creates the conference record. . However, the present invention is not limited to such an embodiment. For example, all the functions described above may be incorporated in one computer system. Further, among the programs executed in the computer system 250, only the function of the phoneme labeling processing unit 78 is executed by another computer, and the computer model 250 receives the phoneme-labeled speech database 80 to learn the acoustic model 48. You may do it. Similarly, the function of the spoken / written language conversion model learning unit 120 may be realized by another system.

  The conference record creation system 30 according to the above-described embodiment is generally called a speech recognition system, and can generate a transcript that is faithful to the content of the utterance by the speech recognition. The deliberation speech corpus is more generally a speech database in which utterances in the deliberation are recorded, and any name may be used. In addition, the conference minutes are an example of document style text, and may be anything as long as the content of the utterance is transcribed and shaped by a human.

  The embodiment disclosed herein is merely an example, and the present invention is not limited to the above-described embodiment. The scope of the present invention is indicated by each claim in the claims after taking into account the description of the detailed description of the invention, and all modifications within the meaning and scope equivalent to the wording described therein are included. Including.

30 Conference record creation system 40 Discussion speech corpus 42 Conference record 44 Acoustic model learning unit for speech recognition 46 Language model learning unit 48 Acoustic model 50 Language models 52 and 144 Speech recognition device 54 Discussion speech 56 Transcription 58 Language model 60 Language model conversion Unit 68 Partial corpus 70 Transcript 72 Partial conference record 76 Corresponding corpus 78 Phoneme labeling processing unit 80 Phoneme-labeled speech database 130 Initial acoustic model learning unit 132 Initial acoustic model 136 Spoken N-gram 138 Turn / speech selection unit 186 Turn N-gram 188 N-gram converter

Claims (6)

A language model estimation means for estimating a language model of a spoken language style transcription that is faithful to the actual content of speech from a language model learned by writing and formatting a speech database by a human;
Phoneme labeling for attaching a transcript and its phoneme label to the speech database by speech recognition using an initial acoustic model prepared in advance and a language model of a spoken style transcript estimated by the language model estimation means Means,
An acoustic model learning device comprising: an acoustic model learning unit for learning or updating a speech recognition acoustic model using the speech database to which the phoneme label is attached by the phoneme labeling unit as learning data. The language model estimating means includes
N-gram creation means for creating an N-gram language model for each turn from document style text corresponding to each turn of the speech database;
Means for estimating a spoken N-gram language model of the spoken style transcription from each of the turn N-gram language models created by the N-gram creation means,
The phoneme labeling means includes:
Language model selection means for selecting a corresponding N-gram language model from the spoken N-gram language model for each turn of the speech database;
Performing speech recognition using the N-gram language model selected by the language model selection means and the initial acoustic model for each turn of the speech database, and transcribed for each turn of the speech database; The acoustic model learning apparatus according to claim 1, further comprising voice recognition means for assigning the phoneme label. Based on a correspondence corpus created based on a part of the spoken language style transcript of the speech database and a part of the document style text corresponding to the part, the representation in the document style text Further comprising a conversion model learning means for learning a conversion model that statistically shows the conversion of spoken style transcription into expression.
The language model estimation means includes means for estimating an N-gram language model of the spoken style transcription by applying the transformation model to each N-gram language model for each turn. Item 4. The acoustic model learning device according to Item 1. The voice database is a deliberative voice corpus that contains some meeting voice,
The acoustic model learning apparatus according to claim 1, wherein the document style text is a minutes of the meeting. Acoustic model storage means for storing the acoustic model for speech recognition learned by the acoustic model learning device according to any one of claims 1 to 4, using a predetermined speech database as learning data,
A speech recognition apparatus, comprising: speech recognition means for performing speech recognition on input speech data using the speech recognition acoustic model stored in the acoustic model storage means and a speech recognition language model. Computer
A language model estimation means for estimating a language model of a spoken language style transcription that is faithful to the actual content of speech from a language model learned by writing and formatting a speech database by a human;
Phoneme labeling for attaching a transcript and its phoneme label to the speech database by speech recognition using an initial acoustic model prepared in advance and a language model of a spoken style transcript estimated by the language model estimation means Means,
A computer program for learning an acoustic model that causes the speech database labeled with a phoneme label by the phoneme labeling means to function as acoustic model learning means for learning or updating an acoustic model for speech recognition using learning data.

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