Speech recognition using adaptation and prior knowledge

• US 20070129943A1
• Filed: 12/06/2005
• Published: 06/07/2007
• Est. Priority Date: 12/06/2005
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: He, Xiaodong, Nguyen, Patrick, Hamaker, Jonathan, Lei, Xin
• Application Number:
  US11/295,297
• Publication Number:
  US 20070129943A1
• Time in Patent Office: Days
• US Class Current: 704/240
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/065 Adaptation

Abstract

A speech recognition system includes a feature extraction component that receives a speech signal and extracts feature vectors from the speech signal. Also included, is a decoder having a speech acoustic model, a feature modification component, and a comparison component. The feature modification component changes the feature vectors, using adaptation data and prior data, to more closely match. the speech acoustic model. The comparison component utilizes the modified feature vectors and the speech acoustic model to recognize the speech signal.

Claims

1. A speech recognition system comprising:

a feature extraction component configured to receive a speech signal and to extract feature vectors from the speech signal; and

a decoder comprising a speech acoustic model, a feature modification component, and a comparison component,

wherein the feature modification component is configured to modify the feature vectors, using adaptation data and prior statistics, to more closely match the speech acoustic model, and wherein the comparison component is configured to utilize the modified feature vectors and the speech acoustic model to recognize the speech signal.

2. The system of claim 1 wherein the prior statistics are obtained from a statistical distribution and incorporated in a Bayesian framework.

4. The system of claim 1 wherein the feature modification component operates using Feature Space Maximum A Posteriori Linear Regression (fMAPLR) based adaptation.

5. The system of claim 4 wherein the feature modification component is configured to carry out the fMAPLR based adaptation in batch mode.

6. The system of claim 4 wherein the feature modification component is configured to carry out the fMAPLR based adaptation in incremental mode.

7. The system of claim 1 and further comprising a prior statistics computation component configured to receive prior data and to compute hyperparameters for the prior data, wherein the hyperparameters constitute the prior statistics.

8. The system of claim 7 wherein the prior data comprises telephony dialogs obtained independently of the speech recognition system.

9. The system of claim 8 wherein the prior statistics computation component is configured to generate feature transform matrices for the telephony dialogs and to compute the hyperparameters from the feature transform matrices.

15. The method of claim 1 wherein modifying the feature vectors using adaptation data and prior data to more closely match the speech recognition system is carried out using Feature Space Maximum A Posteriori Linear Regression (fMAPLR) based adaptation.

16. The method of claim 15 wherein the fMAPLR based adaptation is carried out in batch mode.

17. The method of claim 15 wherein the fMAPLR based adaptation is carried out in incremental mode.

3. The method of claim 3 wherein the statistical distribution is an elliptically symmetric matrix variate distribution.

10. A method of recognizing a speech signal using a speech acoustic model, the method comprising:

extracting feature vectors from the speech signal;

modifying the feature vectors using adaptation data and prior data to more closely match the speech acoustic model; and

utilizing the modified feature vectors and the speech acoustic model to recognize the speech signal.

11. The method of claim 10 wherein modifying the feature vectors using adaptation data and prior data comprises modifying the feature vectors using adaptation data and prior statistics, corresponding to the prior data, incorporated in a Bayesian framework.

12. The method of claim 11 wherein the prior statistics are obtained from a statistical distribution.

13. The method of claim 12 wherein the statistical distribution is an elliptically symmetric matrix variate distribution.

14. The method of claim 12 wherein the prior data comprises telephony dialogs obtained independently of the speech recognition system, and wherein the prior statistics are computed by:

generating feature transform matrices for the telephony dialogs; and

computing hyperparameters from the feature transform matrices.

18. A decoder for use in a speech recognition system, the decoder comprising:

a speech acoustic model;

a feature modification component; and

a comparison component,

wherein the feature modification component is configured to modify feature vectors extracted from a speech signal, using adaptation data and prior statistics, to more closely match the speech acoustic model, and wherein the comparison component is configured to utilize the modified feature vectors and the speech acoustic model to recognize the speech signal.

19. The decoder of claim 18 and wherein the feature modification component is further configured to receive prior data and to compute the prior statistics from the prior data.

20. The decoder of claim 18 and wherein the prior data comprises telephony dialogs obtained independently of the decoder, and wherein the feature modification component is configured to compute the prior statistics by:

generating feature transform matrices for the telephony dialogs; and

computing hyperparameters from the feature transform matrices.