Speech recognition improvement through post-processsing

• US 20040186714A1
• Filed: 03/18/2003
• Published: 09/23/2004
• Est. Priority Date: 03/18/2003
• Status: Abandoned Application

• Current Assignee: Aurilab LLC
• Original Assignee: Aurilab LLC
• Inventors: Baker, James K.
• Application Number:
  US10/389,798
• Publication Number:
  US 20040186714A1
• Time in Patent Office: Days
• US Class Current: 704/236
• CPC Class Codes: G10L 15/08 Speech classification or search

Abstract

A method, program product and system for speech recognition for use with a base speech recognition process, but which does not affect scoring models in the base speech recognition process, the method comprising in one embodiment: obtaining an output hypothesis from a base speech recognition process that uses a first set of scoring models; obtaining a set of alternative hypotheses; scoring the set of alternative hypotheses based on a second set of different scoring models that is separate from and external to the base speech recognition process and does not affect the scoring models thereof; and selecting a hypothesis with a best score.

Claims

1. A method for speech recognition for use with a base speech recognition process, but which does not affect scoring models in the base speech recognition process, comprising:

obtaining an output hypothesis from a base speech recognition process that uses a first set of scoring models;

obtaining a set of alternative hypotheses;

scoring the output hypothesis and each one in the set of alternative hypotheses based on a second set of different scoring models that is separate from and external to the base speech recognition process and does not affect the scoring models thereof; and

selecting a hypothesis with a best score.

2. The method as defined in claim 1, further comprising

presenting the best scoring hypothesis.

collecting error correction or other feedback information,

using the collected information to perform at least one of improving the second set of scoring models or training the base speech recognition process.

3. The method as defined in claim 1, wherein the second set of scoring models may be changed without changing the first set of models or the scores or relative rankings produced by the first set of models.

4. The method as defined in claim 1, wherein the obtaining a list of alternative hypotheses step comprises selecting a reduced number of hypotheses with good scores as determined by the first set of scoring models, wherein the reduced number is less than all of the hypotheses considered by the first speech recognition process.

5. The method as defined in claim 4, further comprising the steps of

comparing two hypotheses with good scores to determine which speech element or elements differ;

and

rescoring with the second set of scoring models at least one of the speech element or elements that differ.

6. The method as defined in claim 1, wherein the obtaining a list of alternative hypotheses step comprises adding at least one new hypothesis to the output hypothesis from the first speech recognition process.

7. The method as defined in claim 6, wherein the adding at least one new hypothesis step comprises the steps of

detecting a confusable one or more speech elements in the output hypothesis;

and

selecting an alternative for at least one of the confusable one or more speech elements; and

creating as an alternative hypothesis a new hypothesis using the alternative speech element.

8. The method as defined in claim 7, wherein the selection of the alternative for the at least one confusable speech element is made from a database of confusable speech elements or speech elements that are often deleted in speech.

9. The method as defined in claim 1, wherein the second set of scoring models includes at least one of an improved set of acoustic models and a language model.

10. The method as defined in claim 1, wherein if the second set of scoring models does not have data pertaining to any of the speech elements which differ between the top choice hypothesis and an alternate hypothesis, then not changing the relative rank between the top choice hypothesis and the said alternate hypothesis.

11. The method as defined in claim 1, wherein the second set of scoring models includes at least one discriminative scoring model.

12. The method as defined in claim 11, further comprising training the discriminative model by a back-propagation algorithm to discriminate between speech elements where error information has been collected for these speech elements.

13. The method as defined in claim 11, further comprising training the discriminative scoring model using less than 50% of the training data normally used to train a standard scoring model.

14. The method as defined in claim 11, wherein the scoring step comprises calculating a different discrimination score between the output hypothesis and each hypothesis in the set of the alternative hypotheses;

and

wherein the selecting a hypothesis step comprises selecting a best hypothesis based at least in part on the discrimination scores.

15. The method as defined in claim 11, wherein the scoring step comprises

obtaining an actual or a simulated score for each of a plurality of hypotheses;

for each of the plurality of hypotheses with the actual or simulated scores, obtaining a total discrimination score for the hypothesis by obtaining a discrimination score for the hypothesis paired with a different hypothesis, and then summing a plurality of the discrimination scores for that given hypothesis;

adding the actual or simulated score for the hypothesis to the total discrimination score for that hypothesis to obtain a revised score; and

wherein the selecting a hypothesis step comprises selecting a hypothesis with the best revised score.

16. The method as defined in claim 2, wherein the collecting information step comprises presenting a screen interface to a user for receiving correction information.

17. The method as defined in claim 2, wherein the collecting information step comprises collecting statistics on errors of the first speech recognition process.

18. The method as defined in claim 2, wherein the using the collected information step comprises the steps of

determining selected errors that are repeated in the first speech recognition process;

and

repeatedly calling a training mechanism in the first speech recognition process to train on the selected errors to thereby give more weight in the training to these selected errors.

19. A program product for speech recognition for use with a base speech recognition process, but which does not affect scoring models in the base speech recognition process, comprising machine-readable program code that, when executed, will cause a machine to perform the following steps:

obtaining an output hypothesis from a base speech recognition process that uses a first set of scoring models;

obtaining a set of alternative hypotheses;

scoring the output hypothesis and each one in the set of alternative hypotheses based on a second set of different scoring models that is separate from and external to the base speech recognition process and does not affect the scoring models thereof; and

selecting a hypothesis with a best score.

20. The program product as defined in claim 19, further comprising program code for performing the steps:

presenting the best scoring hypothesis.

collecting error correction or other feedback information,

using the collected information to perform at least one of improving the second set of scoring models or training the base speech recognition process.

21. The program product as defined in claim 19, wherein the second set of scoring models may be changed without changing the first set of models or the scores or relative rankings produced by the first set of models.

22. The program product as defined in claim 19, wherein the obtaining a list of alternative hypotheses step comprises selecting a reduced number of hypotheses with good scores as determined by the first set of scoring models, wherein the reduced number is less than all of the hypotheses considered by the first speech recognition process.

23. The program product as defined in claim 22, further comprising program code for performing the steps of

comparing two hypotheses with good scores to determine which speech element or elements differ;

and

rescoring with the second set of scoring models at least one of the speech element or elements that differ; and

wherein the selecting a hypothesis step comprises selecting from the compared rescored hypotheses a best a hypothesis with a best score.

24. The program product as defined in claim 19, wherein the obtaining a list of alternative hypotheses step comprises adding at least one new hypothesis to the output hypothesis from the first speech recognition process.

25. The program product as defined in claim 24, wherein the adding at least one new hypothesis step comprises the steps of

detecting a confusable one or more speech elements in the output hypothesis;

and

selecting an alternative for at least one of the confusable one or more speech elements; and

creating as an alternative hypothesis a new hypothesis using the alternative speech element.

26. The method as defined in claim 25, wherein the selection of the alternative for the at least one confusable speech element is made from a database of confusable speech elements or speech elements that are often deleted in speech.

27. The program product as defined in claim 19, wherein the second set of scoring models includes at least one of an improved set of acoustic models and a language model.

28. The program product as defined in claim 19, wherein if the second set of scoring models does not have data pertaining to any of the speech elements which differ between the top choice hypothesis and an alternate hypothesis, then not changing the relative rank between the top choice hypothesis and the said alternate hypothesis.

29. The program product as defined in claim 19, wherein the second set of scoring models includes at least one discriminative scoring model.

30. The program product as defined in claim 29, further comprising program code for training the discriminative model by a back-propagation algorithm to discriminate between speech elements where error information has been collected for these speech elements.

31. The program product s defined in claim 29, further comprising program code for training the discriminative scoring model using less than 50% of the training data normally used to train a standard scoring model.

32. The program product as defined in claim 29, wherein the scoring step comprises calculating a different discrimination score between the output hypothesis and each hypothesis in the set of the alternative hypotheses;

and

wherein the selecting a hypothesis step comprises selecting a best hypothesis based at least in part on the discrimination scores.

33. The program product as defined in claim 29, wherein the scoring step comprises

obtaining an actual or a simulated score for each of a plurality of hypotheses;

for each of the plurality of hypotheses with the actual or simulated scores, obtaining a total discrimination score for the hypothesis by obtaining a discrimination score for the hypothesis paired with a different hypothesis, and then summing a plurality of the discrimination scores for that given hypothesis;

adding the actual or simulated score for the hypothesis to the total discrimination score for that hypothesis to obtain a revised score; and

wherein the selecting a hypothesis step comprises selecting a hypothesis with the best revised score.

34. The program product as defined in claim 20, wherein the collecting information step comprises presenting a screen interface to a user for receiving correction information.

35. The program product as defined in claim 20, wherein the collecting information step comprises collecting statistics on errors of the first speech recognition process.

36. The program product as defined in claim 20, wherein the using the collected information step comprises the steps of

determining selected errors that are repeated in the first speech recognition process;

and

repeatedly calling a training mechanism in the first speech recognition process to train on the selected errors to thereby give more weight in the training to these selected errors.

37. A system for speech recognition for use with a base speech recognition process, but which does not affect scoring models in the base speech recognition process, comprising:

a component for obtaining an output hypothesis from a base speech recognition process that uses a first set of scoring models;

a component for obtaining a set of alternative hypotheses;

a component for scoring the output hypothesis and each one in the set of alternative hypotheses based on a second set of different scoring models that is separate from and external to the base speech recognition process and does not affect the scoring models thereof; and

a component for selecting a hypothesis with a best score.