Adaptation of compressed acoustic models

• US 20040230424A1
• Filed: 05/15/2003
• Published: 11/18/2004
• Est. Priority Date: 05/15/2003
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Gunawardana, Asela J.
• Granted Patent: US 7,499,857 B2
• Time in Patent Office: Days
• US Class Current: 704/219
• CPC Class Codes: G10L 15/07 to the speaker
• CPC Class Codes: G10L 15/144 Training of HMMs
• CPC Class Codes: G10L 15/285 Memory allocation or algorithm optimisation to reduce hardware requirements

Abstract

The present invention is used to adapt acoustic models, quantized in subspaces, using adaptation training data (such as speaker-dependent training data). The acoustic model is compressed into multi-dimensional subspaces. A codebook is generated for each subspace. An adaptation transform is estimated, and it is applied to codewords in the codebooks, rather than to the means themselves.

Claims

1. A method of adapting an acoustic model for use in a speech recognition engine, comprising:

subspace coding the acoustic model to obtain a plurality of codebooks each including a plurality of codewords, the plurality of codebooks including at least one codebook per subspace; and

adapting the codewords in the codebooks based on adaptation training data.

2. The method of claim 1 and further comprising:

prior to adapting, dividing the codewords in each codebook into a plurality of different classes.

3. The method of claim 2 wherein adapting comprises:

applying an adaptation transform to the codewords based on which of the plurality of classes the codewords belong to.

4. The method of claim 3 wherein dividing the codewords comprises:

building a regression tree corresponding to each codebook; and

grouping the codewords in a given codebook into one of a plurality of regression classes by traversing a regression tree corresponding to the given codebook.

5. The method of claim 4 wherein building a regression tree comprises:

building a linguistic regression tree.

6. The method of claim 4 wherein building a regression tree comprises:

building a regression tree by iterative clustering of the codewords.

7. The method of claim 3 wherein applying an adaptation transform comprises:

estimating an adaptation transform corresponding to each of the plurality of classes.

8. The method of claim 1 wherein each codeword represents at least one Gaussian mean and wherein adapting a codeword includes adapting the Gaussian mean.

9. The method of claim 8 wherein each codeword represents at least one Gaussian variance and wherein adapting a codeword includes adapting the Gaussian variance.

10. The method of claim 1 wherein adapting comprises:

adapting the codewords based on speaker-dependent adaptation training data.

11. A computer implemented method of training an acoustic model in a speech recognizer, comprising:

generating a subspace coded acoustic model having a plurality of codebooks, one codebook corresponding to each acoustic subspace into which the acoustic model is coded, each codebook having a plurality of codewords therein, each codeword representing at least one component of an acoustic characteristic of a modeled speech unit; and

modifying the codewords based on adaptation training data.

12. The computer implemented method of claim 11 wherein modifying comprises:

receiving the adaptation training data; and

estimating a transform based on the adaptation training data.

13. The computer implemented method of claim 12 wherein modifying comprises:

grouping the codewords in each codebook into one of a plurality of classes.

14. The computer implemented method of claim 13 wherein estimating a transform comprises:

estimating a transform for each of the plurality of classes.

15. The computer implemented method of claim 14 wherein grouping comprises:

building a regression tree corresponding to each codebook.

16. The computer implemented method of claim 15 wherein grouping comprises:

traversing the regression tree to group the codewords in the corresponding codebook into regression classes.

17. The computer implemented method of claim 16 wherein estimating a transform comprises:

estimating a transform for each regression class.

18. The computer implemented method of claim 17 wherein modifying the codewords comprises:

applying a transform to a given codeword, the transform corresponding to a regression class in which the given codeword resides.

19. A computer readable medium storing instructions which, when executed, cause a computer to perform steps of:

receiving a subspace coded acoustic model including a codebook corresponding to each subspace and a plurality of codewords in each codebook;

receiving training data; and

adapting the codewords in the codebooks based on the training data.

20. The computer readable medium of claim 19 wherein adapting comprises:

grouping the codewords in each codebook into classes; and

adapting the codewords differently depending on a class to which the codewords belong.

21. The computer readable medium of claim 20 wherein grouping the codewords comprises:

obtaining a regression tree for each codebook; and

traversing the regression tree to divide the codewords in each codebook into regression classes.