Recognizing entries in lexical lists

• US 20070136060A1
• Filed: 06/15/2006
• Published: 06/14/2007
• Est. Priority Date: 06/17/2005
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Hennecke, Marcus, Schless, Volker
• Application Number:
  US11/454,612
• Publication Number:
  US 20070136060A1
• Time in Patent Office: Days
• US Class Current: 704/251
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/142 Hidden Markov Models [HMMs]

Abstract

A system recognizes speech using lexical lists. The lexical list may have entries that correspond to words or commands. The system includes an interface for receiving voiced speech and a recognition unit that generates string hypotheses based on the voiced speech. The recognition unit assigns a score to each of the string hypotheses. One of the string hypotheses is compared with an entry in the lexical list by a comparison unit. An assignment unit may then assign one of the string hypotheses to an entry in the lexical list.

Claims

1. A method of recognizing speech, comprising:

detecting a verbal utterance;

converting the verbal utterance into a speech signal;

digitizing the speech signal;

generating at least two string hypotheses corresponding to the speech signal;

assigning a score to each of the at least two string hypotheses; and

comparing at least one of the string hypotheses with an entry in the lexical list based on the score of the at least one string hypothesis.

2. The method according to claim 1, further comprising assigning the at least one string hypothesis to the entry in the lexical list to obtain a data representation of the detected speech signal, where assigning is based on comparing the at least one string hypothesis with the entry in the lexical list, and the score of the at least one of the string hypothesis.

3. The method according to claim 2, where comparing the at least one string hypothesis with the entry in the lexical list is based on a substring of the at least one string hypothesis.

4. The method according to claim 2, where assigning the at least one string hypothesis to the entry in the lexical list is performed based on a substring of the at least one string hypothesis.

5. The method according to claim 2, where assigning the at least one string hypothesis to the entry in the lexical list is also based on a predetermined probability of mistaking the at least one string hypothesis with another string hypothesis.

6. The method according to claim 2, where assigning the at least one string hypothesis to the entry in the lexical list assigns a priority to the score.

7. The method according to claim 1, where comparing is performed when a predetermined condition is satisfied.

8. The method according to claim 7, where the predetermined condition is the at least one string hypothesis having a score greater than a predetermined value.

9. The method according to claim 1, where assigning scores to the at least two string hypotheses is based on an acoustic model probability.

10. The method according to claim 1, where assigning scores to the at least two string hypotheses uses a Hidden Markov Model.

11. The method according to claim 1, where assigning scores to the at least two string hypotheses is based on a grammar model probability.

12. The method according to claim 1, where digitizing the speech signal comprises dividing the speech signal into frames and determining for a feature vector for each frame.

13. The method according to claim 12, where the feature vector comprises spectral content of the speech signal.

14. The method according to claim 12, where the feature vector comprises a cepstral vector corresponding to the speech signal.

15. A system for recognizing speech using long lexical lists stored in a database, comprising:

a database that stores a lexical list;

an interface in communication with the database that detects a speech signal;

a processor in communication with the interface that digitizes the detected speech signal;

a recognition unit in communication with the processor that generates a plurality of string hypotheses corresponding to the speech signal and assigns a score to each of the plurality of string hypotheses;

a comparison unit that compares at least one of the plurality of string hypotheses with an entry in the lexical list based on the score of the at least one string hypothesis; and

,

an assignment unit that assigns the at least one string hypothesis to the entry in the lexical list based on a comparison of the at least one string hypothesis with the entry in the at least one lexical list and the score of the at least one string hypothesis.

16. The system according claim 15, where the comparison unit is operable to perform comparison based on a substring of the at least one string hypothesis.

17. The system according to claim 15, where the assignment unit is operable to perform the assignment based on a substring of the at least one string hypothesis.

18. The system according to claim 15, where the assignment unit is operable to assign the at least one string hypothesis to the entry in the lexical list based on a probability of mistaking the at least one of the string hypothesis with another string hypothesis.

19. The system according to claim 15, where the assignment unit is operable to give a priority to the score of the at least one string hypothesis.

20. The system according to claim 15, where the comparison unit performs a comparison of the at least one string hypothesis with an entry in the lexical list when a predetermined condition occurs.

21. The system according to claim 16 where the predetermined condition comprises the at least one string hypothesis having a score greater than a predetermined value.

22. The system according to claim 15, where the recognition unit determines scores for the plurality of string hypotheses based on an acoustic probability.

23. The system according to claim 15, where the recognition unit determines scores for the plurality of string hypotheses using a Hidden Markov Model.

24. The system according to claim 15, where the recognition unit determines scores for the plurality of string hypotheses based on a grammar model probability.

25. The system according to claim 15, where the processor is programmed to determine a feature vector from the speech signal.

26. The system according to claim 25, where the feature vector comprises spectral content of the speech signal.

27. The system according to claim 25, where the feature vector comprises a cepstral vector corresponding to the speech signal.

28. A system for recognizing speech using long lexical lists stored in a database, comprising:

a database that stores a lexical list;

an interface in communication with the database that detects a speech signal;

a processor in communication with the interface that digitizes the detected speech signal;

means for generating a plurality of string hypotheses corresponding to the speech signal and for assigning a score to each of the plurality of string hypotheses;

means for comparing at least one of the plurality of string hypotheses with an entry in the lexical list based on the score of the at least one string hypothesis; and

,

means for assigning the at least one string hypothesis to the entry in the lexical list based on a comparison of the at least one string hypothesis with the entry in the at least one lexical list and the score of the at least one string hypothesis.