JP2018004947A - Text correction device, text correction method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To efficiently generate transcribed text that includes unnecessary words.SOLUTION: A morpheme analysis unit 1 analyzes the morpheme of transcribed text that was transcribed from a spoken voice. A grammar generation unit 2 inserts an unnecessary word in the morpheme analysis result for each morpheme boundary and generates a grammar model. A speech recognition unit 3 generates a plurality of speech recognition result candidates in which the speech data of spoken voices is speech recognized using the grammar model. A recognition result selection unit 4 calculates, for each speech recognition result candidate, the degree of similarity of an unnecessary word that is a syllable train included in the speech recognition result candidate to a word that follows the unnecessary word, and selects a speech recognition result of the spoken voice from the speech recognition result candidates on the basis of the calculated degree of similarity.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a speech recognition technique, and more particularly to a technique for correcting a transcription text used for learning an acoustic model.

  In general, speech recognition uses three types of models: an acoustic model that models the acoustic features of speech, a language model that models the ease of connection between words, and a pronunciation dictionary that associates words with phoneme strings. Done.

  The sound is tuned mainly by the shape of the mouth and tongue, the position of the tongue, and the movement of the lips. For this reason, it is assumed that the acoustic features may change depending on the phoneme environment before and after, and the acoustic model is determined for each N-gram considering the phoneme, the phoneme sequence that appears before that, and the phoneme sequence that appears after that. Often modeled. Therefore, an acoustic model is generally learned as teacher data that a correspondence relationship between speech or a feature amount thereof and a phoneme is given. However, since it is very costly to manually assign this correspondence relationship, an automated method such as the Viterbi algorithm is used for manual operation based on the speech or its feature value and the phoneme string that accurately represents the utterance content. In many cases, it is used for learning.

  The phoneme string for learning the acoustic model described above generally transcribes the utterance content of the learning speech manually (in Japanese) as kana-kanji text, and gives a reading using a morphological analyzer. It is created by generating phoneme strings from readings using a pronunciation dictionary.

  In order to learn the acoustic model more accurately, it is necessary to create a transcription text accurately. However, it is often observed that spoken language has a phenomenon unique to spoken language (hereinafter referred to as “unnecessary word”) that is not directly related to the content of the utterance, such as filler, squeeze / rephrase. For this reason, it is desirable to accurately describe this unnecessary word when creating a transcription text for acoustic model learning. However, since unnecessary words are not very conscious in daily life, accurate transcription of unnecessary words requires proficiency, and the time required for transcription itself increases.

  Various techniques for restoring (inserting) unnecessary words from incompletely transcribed text that does not include unnecessary words have been developed. For example, in Non-Patent Document 1, focusing on fillers among unnecessary words, it is defined as a series labeling problem for labeling whether or not a filler follows each morpheme of incompletely transcribed text, and a conditional random field ( Filler insertion is realized using CRF (Conditional Random Fields). Further, for example, in Non-Patent Document 2, using a statistical style conversion model, meeting minutes described in written language are converted into spoken words, and detailed units of the meeting (for example, each speaker change) are converted from the converted minutes. Utterance of about 10 seconds to 3 minutes as a turn of), generates a language model with strong constraints, and recognizes speech using the actual speech and its language model, thereby generating utterance content text including unnecessary words .

Kengo Ota, Masami Tsuchiya, Seiichi Nakagawa, “Construction of spoken language model based on filler prediction model”, Transactions of Information Processing Society of Japan, Vol.50, No.2, pp.477-487, 2009 Masato Mimura, Yuya Akita, Tatsuya Kawahara, “Learning Supervised Acoustic Model Using Statistical Language Model Transformation”, IEICE Journal, Vol.J94-D, No.2, pp.460-468, 2011 Year

  However, in Non-Patent Document 1, the filler insertion location and filler type can be estimated with higher accuracy than in the prior art, but the occurrence of filler originally may occur stochastically and statistically estimated only by text. Difficult to do. Also, unnecessary words other than the filler cannot be restored.

  In Non-Patent Document 2, conversion to spoken language is performed using a statistical spoken language conversion model, but it is unclear whether unnecessary words other than filler can be modeled statistically because of the high occurrence probability of filler among unnecessary words. It is. In fact, the only unnecessary word that can be restored in Non-Patent Document 2 is the filler. Further, the turn for each speaker change is longer than a general speech recognition unit (sentence), and it is unclear whether the restriction by the language model is correctly applied.

  In view of the above-described points, an object of the present invention is to efficiently generate a transcription text including unnecessary words.

  In order to solve the above-mentioned problem, the text correction apparatus of the present invention generates a grammar model by inserting unnecessary words at each morpheme boundary into a morpheme analysis result of a transcribed text in which speech speech is transcribed. A speech recognition unit for generating a plurality of speech recognition result candidates obtained by speech recognition of speech data of an utterance speech using a grammar model, and a syllable string included in each speech recognition result candidate for each speech recognition result candidate A recognition result selection unit that calculates the similarity between the word and the word following the unnecessary word, and selects the speech recognition result of the uttered speech from the speech recognition result candidates based on the similarity.

  According to the present invention, an arbitrary unnecessary word can be restored from a transcription text that does not include an unnecessary word in accordance with an actual utterance. Therefore, it is possible to efficiently generate a transcription text including unnecessary words.

FIG. 1 is a diagram illustrating a functional configuration of the text correction apparatus. FIG. 2 is a diagram illustrating a processing procedure of the text correction method. FIG. 3 is a diagram for explaining the processing contents of the grammar generation unit. FIG. 4 is a diagram for explaining the processing contents of the grammar generation unit. FIG. 5 is a diagram for explaining the processing contents of the grammar generation unit. FIG. 6 is a diagram for explaining the processing contents of the recognition result selection unit. FIG. 7 is a diagram for explaining the processing contents of the recognition result selection unit. FIG. 8 is a diagram for explaining the processing contents of the recognition result selection unit.

  Hereinafter, embodiments of the present invention will be described in detail. In addition, the same number is attached | subjected to the component which has the same function in drawing, and duplication description is abbreviate | omitted.

  As shown in FIG. 1, the text correction apparatus according to the embodiment includes a morphological analysis unit 1, a grammar generation unit 2, a speech recognition unit 3, a recognition result selection unit 4, and a pronunciation dictionary storage unit 5. The text correction method of the embodiment is realized by the processing of each step described later by this text correction device.

  The text correction device is, for example, a special program configured by reading a special program into a known or dedicated computer having a central processing unit (CPU), a main storage device (RAM: Random Access Memory), and the like. Device. For example, the text correction device executes each process under the control of the central processing unit. Data input to the text correction device and data obtained in each process are stored in, for example, a main storage device, and the data stored in the main storage device is read out as needed and used for other processing. The At least a part of each processing unit of the text correction apparatus may be configured by hardware such as an integrated circuit. Each storage unit included in the text correction device is, for example, a main storage device such as a RAM (Random Access Memory), an auxiliary storage device configured by a semiconductor memory element such as a hard disk, an optical disk, or a flash memory, or a relational device. It can be configured with middleware such as a database or key-value store.

  With reference to FIG. 2, the processing procedure of the text correction method of the embodiment will be described.

  In step S <b> 1, the morphological analysis unit 1 receives the transcription text transcribed from the uttered speech, and performs morphological analysis on the transcription text. The morphological analysis result is sent to the grammar generation unit 2. A general morpheme analyzer may be used for the morpheme analysis, but at least one that can output notation and reading is used. Here, when the part of speech information is included in the morphological analysis result, the morpheme of the specific part of speech may be combined with the morpheme of another part of speech to reduce the number of morphemes. The smaller the number of morphemes included in the morpheme analysis result, the smaller the overall processing amount.

For example, if the input transcript is “Tomorrow is sunny”, the morphological analysis result is as follows.
Tomorrow; As; Noun; Wa; Case particle Sunny; Kaisei;

In this morpheme analysis result, for example, the number of morphemes can be reduced by combining the morpheme of the case particle with the morpheme of the immediately preceding noun. The results are shown below.
Tomorrow; Asuwa; Noun Clear; Kaisei; Noun

  In step S2, the grammar generation unit 2 receives the morpheme analysis result output from the morpheme analysis unit 1, reads out the pronunciation dictionary stored in the pronunciation dictionary storage unit 5, and is unnecessary for each morpheme boundary with respect to the morpheme analysis result. Insert a word to generate a grammar model. The grammar model is sent to the speech recognition unit 3.

  The grammar model is generated as follows. First, as shown in FIG. 3, an acceptable grammar such as a finite state grammar is generated with reference to the notation of the morphological analysis result. The example of FIG. 3 is an example in which a grammar generated from a transcription text “Tomorrow is sunny” is expressed by a weighted finite state transducer (WFST). Next, as shown in FIG. 4, the grammar is updated so that it can be accepted by inserting a filler, a syllable, and a pause for each morpheme boundary of the morpheme analysis result. In the example of FIG. 4, for the grammar illustrated in FIG. 3, between “Tomorrow is” and “Clear”, fillers (such as “Ah” “Ah”) and syllables (“A” “Te” “F”). Etc.) is inserted. In the example of FIG. 4, the weights α, β, and γ given to each unnecessary word are constants. Further, as shown in FIG. 5, the grammar is updated so that the filler, syllable, and pause can be accepted even if they can be continued. The example of FIG. 5 is an example of a grammar that can accept two consecutive filler continuations and syllable continuations between “Tomorrow is” and “Clear”. Finally, the pronunciation dictionary is used to convert each morpheme reading into a phoneme and update the grammar. The grammar model to be generated may be anything as long as it can be handled by the subsequent speech recognition unit 3. In the above description, the grammar is generated first and then updated. However, the final grammar may be generated at a time.

  In step S3, the speech recognition unit 3 receives speech speech data and the grammar model output from the grammar generation unit 2, and uses the grammar model to perform speech recognition on speech speech data. A speech recognition result candidate is obtained. The speech recognition result candidate is sent to the recognition result selection unit 4. Any speech recognizer may be used for speech recognition.

For example, if the actual utterance content is “Tomorrow is good, clear weather” and the transcript is “Tomorrow is clear”, the speech recognition result candidates are as follows: become. Here, the case where the speech recognition result candidates up to the third place (3-best) are output is shown.
1st place Tomorrow is Kahi, it's fine weather 2nd place Tomorrow is nice, it's fine weather 3rd place Tomorrow is really fine

  When the speech recognizer used in the speech recognition unit 3 can output a word lattice, a word lattice is generated and output as a speech recognition result candidate. FIG. 6 shows an example in which the above speech recognition result candidate is output as a word lattice. In FIG. 6, a bold solid line block represents a syllable string, a double line block represents a filler, and a bold dotted line block represents a pause. Each path (arrow) corresponds to a score after speech recognition, but is omitted in FIG.

  In step S4, the recognition result selection unit 4 receives the speech recognition result candidate output from the speech recognition unit 3, and selects the speech recognition result of the uttered speech from the speech recognition result candidate. The selected speech recognition result is output as a transcription text with unnecessary words.

  When the input is a speech recognition result candidate, for each speech recognition result candidate, the similarity between the syllable string inserted from the transcribed text and the word following the syllable string is obtained except for the filler and pause. When a plurality of syllable strings exist for one speech recognition result candidate, one value is obtained by some means such as an average value, a median value, or a maximum value of similarities obtained for each phoneme string. The speech recognition result candidate having the highest similarity obtained as described above is output as the final speech recognition result.

  If the input is a word lattice, the syllable string is obtained by calculating the similarity between the syllable string and the word that follows the syllable string, excluding the filler and pause. Rescore the path score through the column. In FIG. 7, a word indicated by a black block is a word (“sunny”) that follows the word except for fillers and pauses for a word having a plurality of candidates (“Kai”, “Kahi”...). It is an example. In slogan or rephrasing, correct contents often appear immediately after an unnecessary word, so a syllable string that is a recognition result of an unnecessary word other than a filler is compared with the subsequent word. Finally, the maximum likelihood path is obtained from the re-scored path, and the speech recognition result is output. In the example of FIG. 8, a bold solid line arrow indicates the maximum likelihood path, and “Tomorrow is bright, it is clear” is selected as the maximum likelihood path.

  Regardless of the input, any method for calculating the similarity may be used. However, the larger the numerical value, the more similar the index needs to be. As a method of calculating the similarity, for example, 1. 1. Consistency of syllable notation between syllable string and subsequent word For example, the edit distance between the phoneme string of the syllable string and the phoneme string of the subsequent word. In the former, for example, the number of syllables with the same notation is used as the similarity. The latter may use a general editing distance calculation method. At that time, if the classification by the articulation method (closing sound, friction sound, etc.) is similar, it is possible to devise such as reducing the distance.

  By configuring as described above, according to the text correction technique of the present invention, a grammar model including an unnecessary word is generated from a transcription text that does not include an unnecessary word, and an actual utterance is recognized by voice recognition using the grammar model. By doing so, any unnecessary words can be restored. Therefore, it is possible to efficiently generate a transcription text including unnecessary words.

  As described above, the embodiments of the present invention have been described, but the specific configuration is not limited to these embodiments, and even if there is a design change or the like as appropriate without departing from the spirit of the present invention, Needless to say, it is included in this invention. The various processes described in the embodiments are not only executed in time series according to the description order, but may also be executed in parallel or individually as required by the processing capability of the apparatus that executes the processes.

[Program, recording medium]
When various processing functions in each device described in the above embodiment are realized by a computer, the processing contents of the functions that each device should have are described by a program. Then, by executing this program on a computer, various processing functions in each of the above devices are realized on the computer.

  The program describing the processing contents can be recorded on a computer-readable recording medium. As the computer-readable recording medium, for example, any recording medium such as a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory may be used.

  This program is distributed by selling, transferring, or lending a portable recording medium such as a DVD or CD-ROM in which the program is recorded. Furthermore, the program may be distributed by storing the program in a storage device of the server computer and transferring the program from the server computer to another computer via a network.

  A computer that executes such a program first stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own storage device. When executing the process, the computer reads a program stored in its own recording medium and executes a process according to the read program. As another execution form of the program, the computer may directly read the program from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to the computer. Each time, the processing according to the received program may be executed sequentially. A configuration in which the above-described processing is executed by a so-called ASP (Application Service Provider) type service that realizes a processing function only by an execution instruction and result acquisition without transferring a program from the server computer to the computer. It is good. Note that the program in this embodiment includes information that is used for processing by an electronic computer and that conforms to the program (data that is not a direct command to the computer but has a property that defines the processing of the computer).

  In this embodiment, the present apparatus is configured by executing a predetermined program on a computer. However, at least a part of these processing contents may be realized by hardware.

1 Morphological Analysis Unit 2 Grammar Generation Unit 3 Speech Recognition Unit 4 Recognition Result Selection Unit 5 Pronunciation Dictionary Storage Unit

Claims (7)

A grammar generation unit that generates a grammar model by inserting unnecessary words at each morpheme boundary to the morphological analysis result of the transcribed text transcribed speech speech;
A speech recognition unit that generates a plurality of speech recognition result candidates obtained by speech recognition of speech data of the uttered speech using the grammar model;
For each speech recognition result candidate, a similarity between an unnecessary word that is a syllable string included in the speech recognition result candidate and a word that follows the unnecessary word is calculated, and the uttered speech is calculated from the speech recognition result candidate based on the similarity. A recognition result selection unit for selecting the voice recognition result of
Text correction device. The text correction device according to claim 1,
The grammar generation unit generates the grammar model by inserting the unnecessary words including filler, syllable, or silence for each morpheme analysis boundary with respect to the morpheme analysis result.
Text correction device. The text correction device according to claim 2,
The grammar generation unit generates the grammar model by inserting the unnecessary words including a filler continuation in which an arbitrary filler is continuous and a syllable continuation in which a plurality of syllables are continuous for each morphological analysis boundary with respect to the morphological analysis result. To do,
Text correction device. The text correction apparatus according to any one of claims 1 to 3,
The recognition result selection unit calculates, as the similarity, the number of syllables in which the syllable representations of the unnecessary words that are syllable strings included in the speech recognition result candidate and the syllable strings of words following the unnecessary words match. Is,
Text correction device. The text correction apparatus according to any one of claims 1 to 3,
The recognition result selection unit calculates, as the similarity, an edit distance between a phoneme string of an unnecessary word that is a syllable string included in the speech recognition result candidate and a phoneme string of a syllable string of a word that follows the unnecessary word. is there,
Text correction device. The grammar generation unit generates a grammar model by inserting unnecessary words at each morpheme boundary to the morphological analysis result of the transcription text that transcribes the speech.
The speech recognition unit generates a plurality of speech recognition result candidates obtained by performing speech recognition on the speech data of the uttered speech using the grammar model,
The recognition result selection unit calculates, for each speech recognition result candidate, the similarity between the syllable string of each unnecessary word included in the speech recognition result candidate and the syllable string of the word following the unnecessary word, and based on the similarity Selecting a speech recognition result of the uttered speech from the speech recognition result candidates,
Text correction method.   A program for causing a computer to function as the text correction apparatus according to claim 1.

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