LANGUAGE MODEL CREATION DEVICE

• US 20110231183A1
• Filed: 09/03/2009
• Published: 09/22/2011
• Est. Priority Date: 11/28/2008
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Yamamoto, Hitoshi, Miki, Kiyokazu, Hanazawa, Ken
• Granted Patent: US 9,043,209 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/19 Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams

Abstract

This device 301 stores a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content, and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content. Based on a first probability parameter representing a probability that a content represented by a target word sequence included in a speech recognition hypothesis generated by a speech recognition process of recognizing a word sequence corresponding to a speech, a second probability parameter representing a probability that the content represented by the target word sequence is a second content, the first content-specific language model and the second content-specific language model, the device creates a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech.

Claims

1. A language model creation device, comprising:

a content-specific language model storing unit configured to store a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content, and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content; and

a language model creating unit configured to execute a language model creation process of;

acquiring a first probability parameter representing a probability that a content represented by a target word sequence that is at least part of an inputted word sequence, which is a word sequence included in a speech recognition hypothesis generated by execution of a speech recognition process of recognizing a word sequence corresponding to a speech and is a word sequence having been inputted, is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content; and

creating a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech, based on the acquired first probability parameter, the acquired second probability parameter, the stored first content-specific language model, and the stored second content-specific language model.

2. The language model creation device according to claim 1, wherein:

the language model creating unit is configured to create the language model that the larger a sum of a value obtained by multiplying a first coefficient becoming larger as the acquired first probability parameter becomes larger by the probability represented by the first content-specific language model and a value obtained by multiplying a second coefficient becoming larger as the acquired second probability parameter becomes larger by the probability represented by the second content-specific language model becomes, the larger the probability that the specific word appears in the word sequence corresponding to the part corresponding to the target word sequence of the speech becomes.

3. The language model creation device according to claim 1, wherein:

the language model creating unit is configured to execute the language model creation process on each of the plurality of target word sequences obtained by dividing the inputted word sequence on a border between words of the word sequence recognized in the speech recognition process.

4. The language model creation device according to claim 1, wherein:

the language model creating unit is configured to execute the language model creation process on each of the plurality of target word sequences obtained by dividing the inputted word sequence at a position different from a border between words of the word sequence recognized in the speech recognition process.

5. The language model creation device according to claim 3, wherein:

the language model creating unit is configured to, in a case that both the acquired first probability parameter and the acquired second probability parameter are smaller than a preset lower limit threshold, set both the acquired first probability parameter and the acquired second probability parameter to same values.

6. The language model creation device according to claim 3, wherein:

the language model creating unit is configured to, in a case that the first probability parameter acquired with respect to a certain target word sequence is larger than a preset upper limit threshold, correct so as to increase the first probability parameter acquired with respect to a target word sequence adjacent to the certain target word sequence.

7. The language model creation device according to claim 1, wherein:

the language model creating unit is configured to acquire the first probability parameter and the second probability parameter based on a theory of conditional random fields.

8. The language model creation device according to claim 1, wherein:

the language model creating unit is configured to acquire the first probability parameter and the second probability parameter based on at least one of word attribute information representing an attribute of a word included in the target word sequence and speech recognition process information acquired at a time of execution of the speech recognition process of recognizing the target word sequence from the speech.

9. The language model creation device according to claim 8, wherein:

the word attribute information includes at least one of information representing a word surface, information representing how to read, and information representing a part of speech.

10. The language model creation device according to claim 8, wherein:

the speech recognition process information includes at least one of information representing recognition reliability that is reliability of a result of recognition by the speech recognition process, information representing a length of duration that is a time period during which one sound continues, and information representing presence/absence of an antecedent silence.

11. The language model creation device according to claim 1, wherein:

the language model creating unit is configured to acquire the first probability parameter and the second probability parameter based on at least one of information representing a position of the target word sequence in the inputted word sequence, information representing that the inputted word sequence includes a plurality of same words, information representing a connective state of contents in the inputted word sequence, and information representing, in a case that there are the plurality of inputted word sequences, a relation between the inputted word sequences.

12. A speech recognition device, comprising:

a content-specific language model storing unit configured to store a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content, and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content;

a language model creating unit configured to execute a language model creation process of;

acquiring a first probability parameter representing a probability that a content represented by a target word sequence that is at least part of an inputted word sequence, which is a word sequence included in a speech recognition hypothesis generated by execution of a speech recognition process of recognizing a word sequence corresponding to a speech and is a word sequence having been inputted, is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content; and

creating a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech, based on the acquired first probability parameter, the acquired second probability parameter, the stored first content-specific language model, and the stored second content-specific language model; and

a speech recognizing unit configured to execute a speech recognition process of recognizing a word sequence corresponding to an inputted speech, based on the language model created by the language model creating unit.

13. The speech recognition device according to claim 12, wherein:

the language model creating unit is configured to create the language model that the larger a sum of a value obtained by multiplying a first coefficient becoming larger as the acquired first probability parameter becomes larger by the probability represented by the first content-specific language model and a value obtained by multiplying a second coefficient becoming larger as the acquired second probability parameter becomes larger by the probability represented by the second content-specific language model becomes, the larger the probability that the specific word appears in the word sequence corresponding to the part corresponding to the target word sequence of the speech becomes.

14. The speech recognition device according to claim 12, wherein:

the speech recognizing unit is configured to generate the inputted word sequence by executing the speech recognition process of recognizing the word sequence corresponding to the inputted speech;

the language model creating unit is configured to create the language model based on the inputted word sequence generated by the speech recognizing unit; and

the speech recognizing unit is configured to again execute the speech recognition process of recognizing the word sequence corresponding to the inputted speech, based on the language model created by the speech model creating unit.

15. The speech recognition device according to claim 12, configured to execute an iterative process of alternately repeating the speech recognition process that the speech recognizing unit recognizes the word sequence corresponding to the inputted speech based on the language model created by the language model creating unit and the language model creation process that the language model creating unit creates the language model based on the word sequence recognized by the speech recognizing unit.

16. The speech recognition device according to claim 15, configured to finish the iterative process in a case that a predetermined finish condition is satisfied.

17. The speech recognition device according to claim 16, wherein:

the finish condition is a condition that a word sequence recognized by the previous speech recognition process coincides with a word sequence recognized by the present speech recognition process.

18. The speech recognition device according to claim 16, wherein:

the finish condition is a condition that a number of times of execution of the speech recognition process is larger than a preset threshold number of times.

19. A language model creation method, comprising, in a case that a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content are stored in a storing device:

acquiring a first probability parameter representing a probability that a content represented by a target word sequence that is at least part of an inputted word sequence, which is a word sequence included in a speech recognition hypothesis generated by execution of a speech recognition process of recognizing a word sequence corresponding to a speech and is a word sequence having been inputted, is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content; and

creating a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech, based on the acquired first probability parameter, the acquired second probability parameter, the stored first content-specific language model, and the stored second content-specific language model.

20. The language model creation method according to claim 19, comprising:

creating the language model that the larger a sum of a value obtained by multiplying a first coefficient becoming larger as the acquired first probability parameter becomes larger by the probability represented by the first content-specific language model and a value obtained by multiplying a second coefficient becoming larger as the acquired second probability parameter becomes larger by the probability represented by the second content-specific language model becomes, the larger the probability that the specific word appears in the word sequence corresponding to the part corresponding to the target word sequence of the speech becomes.

21. A computer-readable medium storing a language model creation program comprising instructions which cause an information processing device to realize:

a content-specific language model storing processing unit configured to cause a storing device to store a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content, and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content; and

a language model creating unit configured to;

acquire a first probability parameter representing a probability that a content represented by a target word sequence that is at least part of an inputted word sequence that is a word sequence included in a speech recognition hypothesis generated by execution of a speech recognition process of recognizing a word sequence corresponding to a speech and that is a word sequence having been inputted is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content; and

create a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech, based on the acquired first probability parameter, the acquired second probability parameter, the stored first content-specific language model, and the stored second content-specific language model.

22. The computer readable medium according to claim 21, wherein:

the language model creating unit is configured to create the language model that the larger a sum of a value obtained by multiplying a first coefficient becoming larger as the acquired first probability parameter becomes larger by the probability represented by the first content-specific language model and a value obtained by multiplying a second coefficient becoming larger as the acquired second probability parameter becomes larger by the probability represented by the second content-specific language model becomes, the larger the probability that the specific word appears in the word sequence corresponding to the part corresponding to the target word sequence of the speech becomes.

23. A language model creation device, comprising:

a content-specific language model storing means configured to store a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content, and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content; and

a language model creating means configured to execute a language model creation process of;

acquiring a first probability parameter representing a probability that a content represented by a target word sequence that is at least part of an inputted word sequence, which is a word sequence included in a speech recognition hypothesis generated by execution of a speech recognition process of recognizing a word sequence corresponding to a speech and is a word sequence having been inputted, is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content; and

creating a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech, based on the acquired first probability parameter, the acquired second probability parameter, the stored first content-specific language model, and the stored second content-specific language model.

24. A speech recognition device, comprising:

a content-specific language model storing means configured to store a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content, and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content;

a language model creating means configured to execute a language model creation process of;

acquiring a first probability parameter representing a probability that a content represented by a target word sequence that is at least part of an inputted word sequence, which is a word sequence included in a speech recognition hypothesis generated by execution of a speech recognition process of recognizing a word sequence corresponding to a speech and is a word sequence having been inputted, is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content; and

creating a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech, based on the acquired first probability parameter, the acquired second probability parameter, the stored first content-specific language model, and the stored second content-specific language model; and

a speech recognizing means configured to execute a speech recognition process of recognizing a word sequence corresponding to an inputted speech, based on the language model created by the language model creating means.