SPEECH RECOGNITION USING DISCRIMINANT FEATURES

• US 20080059168A1
• Filed: 10/31/2007
• Published: 03/06/2008
• Est. Priority Date: 03/29/2001
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: International Business Machines Corporation
• Inventors: Eide, Ellen
• Application Number:
  US11/931,014
• Publication Number:
  US 20080059168A1
• Time in Patent Office: Days
• US Class Current: 704/231
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit

Abstract

Methods and arrangements for representing the speech waveform in terms of a set of abstract, linguistic distinctions in order to derive a set of discriminative features for use in a speech recognizer. By combining the distinctive feature representation with an original waveform representation, it is possible to achieve a reduction in word error rate of 33% on an automatic speech recognition task.

Claims

1. A method of facilitating speech recognition, said method comprising the steps of:

obtaining speech input data;

building a model for each feature of an original set of linguistic features, wherein the model reflects whether or not each feature is present;

ranking the linguistic features; and

rebuilding the model for each of a preselected number N of the ranked linguistic features.

2. The method according to claim 1, wherein said step of building a model for each of a preselected number N of the ranked features comprises building a model for the top N ranked features.

3. The method according to claim 1, further comprising the step of compiling a confusion matrix for each feature of the original set of features subsequent to said step of building a model for each feature of an original set of features.

4. The method according to claim 3, wherein said step of compiling a confusion matrix comprises computing a score for each feature based on the likelihood of its presence in a frame of the speech input data.

5. The method according to claim 4, wherein said step of computing a score for each feature comprises computing a score as a log-likelihood ratio.

6. The method according to claim 4, wherein said step of compiling a confusion matrix further comprises comparing each score of each feature with a threshold.

7. The method according to claim 4, wherein said step of compiling a confusion matrix further comprises calculating mutual information between truth and labels for each feature.

8. The method according to claim 1, wherein said step of building a model for each feature of an original set of features comprises:

partitioning the speech input data in parallel, once for each feature; and

producing an observation vector.

9. The method according to claim 8, wherein said step of building a model for each feature of an original set of features comprises:

partitioning data in parallel from the observation vector, once for each feature; and

producing final observations.

10. The method according to claim 1, wherein said step of building a model for each of a preselected number N of the ranked features comprises:

partitioning the speech input data in parallel, once for each feature; and

producing an observation vector.

11. The method according to claim 10, wherein said step of building a model for each of a preselected number N of the ranked features comprises:

partitioning data in parallel from the observation vector, once for each feature; and

producing final observations.

12. An apparatus for facilitating speech recognition, said method comprising the steps of:

an input medium which obtains speech input data;

a first model builder which builds a model for each feature of an original set of linguistic features, wherein the model reflects whether or not each feature is present;

a ranking arrangement which ranks the linguistic features; and

a second model builder which rebuilds the model for each of a preselected number N of the ranked linguistic features.

13. The apparatus according to claim 12, wherein said second model builder is adapted to build a model for the top N ranked features.

14. The apparatus according to claim 12, further comprising a matrix compiler which compiles a confusion matrix for each feature of the original set of features subsequent to said step of building a model for each feature of an original set of features.

15. The apparatus according to claim 14, wherein said matrix compiler is adapted to compute a score for each feature based on the likelihood of its presence in a frame of the speech input data.

16. The apparatus according to claim 15, wherein said matrix compiler is adapted to compute a score as a log-likelihood ratio.

17. The apparatus according to claim 15, wherein said matrix compiler is adapted to compare each score of each feature with a threshold.

18. The apparatus according to claim 15, wherein said matrix compiler is adapted to calculate mutual information between truth and labels for each feature..

19. The apparatus according to claim 12, wherein said first model builder is adapted to:

partition the speech input data in parallel, once for each feature; and

produce an observation vector.

20. The apparatus according to claim 19, wherein said first model builder is adapted to:

partition data in parallel from the observation vector, once for each feature; and

produce final observations.

21. The apparatus according to claim 12, wherein said second model builder is adapted to:

partition the speech input data in parallel, once for each feature; and

produce an observation vector.

22. The apparatus according to claim 21, wherein said second model builder is adapted to:

partition data in parallel from the observation vector, once for each feature; and

produce final observations.

23. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for speech recognition, said method comprising the steps of:

obtaining speech input data;

building a model for each feature of an original set of linguistic features, wherein the model reflects whether or not each feature is present;

ranking the linguistic features; and

rebuilding the model for each of a preselected number N of the ranked linguistic features.