MULTILINGUAL WEIGHTED CODEBOOKS

• US 20090254335A1
• Filed: 04/01/2009
• Published: 10/08/2009
• Est. Priority Date: 04/01/2008
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Harman Becker Automotive Systems GmbH (Samsung Electronics Co. Ltd.)
• Inventors: Brückner, Raymond, Gruhn, Rainer, Raab, Martin
• Application Number:
  US12/416,768
• Publication Number:
  US 20090254335A1
• Time in Patent Office: Days
• US Class Current: 704/8
• CPC Class Codes: G10L 15/005 Language recognition
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 2019/0007 Codebook element generation

Abstract

Examples of methods are provided for generating a multilingual codebook. According to an example method, a main language codebook and at least one additional codebook corresponding to a language different from the main language are provided. A multilingual codebook is generated from the main language codebook and the at least one additional codebook by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook. Systems and methods for speech recognition using the multilingual codebook and applications that use speech recognition based on the multilingual codebook are also provided.

Claims

1. A method for generating a multilingual codebook comprising:

providing a main language codebook;

providing at least one additional codebook corresponding to a language different from the main language; and

generating a multilingual codebook from the main language codebook and the at least one additional codebook by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook.

2. The method of claim 1 further comprising:

determining distances between code vectors of the at least one additional codebook and code vectors of the main language codebook; and

adding at least one code vector of the at least one additional codebook to the main language codebook having a predetermined distance from the code vector of the main language codebook that is closest to the at least one code vector.

3. The method of claim 1 further comprising:

merging a code vector of the at least one additional codebook and a code vector of the main language codebook when the distance between them lies below a predetermined threshold.

4. The method of claim 3 further comprising:

adding the merged code vector to the main language codebook.

5. The method of claim 1 further comprising:

generating the main language codebook and/or the at least one additional codebook based on utterances by a particular user.

6. The method of claim 1 further comprising:

processing the code vectors of the codebooks according to a Gaussian density distribution.

7. The method of claim 1 further comprising:

determining the distances based on either the Mahalanobis distance or the Kullback-Leibler divergence.

8. A method for speech recognition comprising:

providing a multilingual codebook generated by a method comprising;

providing a main language codebook;

providing at least one additional codebook corresponding to a language different from the main language; and

generating a multilingual codebook from the main language codebook and the at least one additional codebook by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook;

detecting a speech input; and

processing the speech input for speech recognition using the provided multilingual codebook.

9. The method of claim 8 where the method of providing a multilingual codebook further comprises:

determining distances between code vectors of the at least one additional codebook and code vectors of the main language codebook; and

adding at least one code vector of the at least one additional codebook to the main language codebook having a predetermined distance from the code vector of the main language codebook that is closest to the at least one code vector.

10. The method of claim 8 where the method of providing a multilingual codebook further comprises:

merging a code vector of the at least one additional codebook and a code vector of the main language codebook when the distance between them lies below a predetermined threshold.

11. The method claim 10 where the method of providing a multilingual codebook further comprises:

adding the merged code vector to the main language codebook.

12. The method of claim 8 where the method of providing a multilingual codebook further comprises:

generating the main language codebook and/or the at least one additional codebook based on utterances by a particular user.

13. The method of claim 8 where the method of providing a multilingual codebook further comprises:

processing the code vectors of the codebooks according to a Gaussian density distribution.

14. The method claim 8 where the method of providing a multilingual codebook further comprises:

determining the distances based on either the Mahalanobis distance or the Kullback-Leibler divergence.

15. The method of claim 8 further comprising:

processing the speech input for speech recognition includes speech recognition based on a Hidden Markov Model.

16. A speech recognition system comprising:

a codebook generator configured to generate a multilingual codebook by accessing a main language codebook and at least one additional codebook corresponding to a language different from the main language, and by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook.

17. A vehicle navigation system comprising:

a speech recognition having a codebook generator configured to generate a multilingual codebook by accessing a main language codebook and at least one additional codebook corresponding to a language different from the main language, and by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook.

18. An audio device comprising:

a speech recognition having a codebook generator configured to generate a multilingual codebook by accessing a main language codebook and at least one additional codebook corresponding to a language different from the main language, and by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook.

19. A mobile communications device comprising:

a speech recognition having a codebook generator configured to generate a multilingual codebook by accessing a main language codebook and at least one additional codebook corresponding to a language different from the main language, and by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook.