System and method of word lattice augmentation using a pre/post vocalic consonant distinction

• US 8,024,191 B2
• Filed: 10/31/2007
• Issued: 09/20/2011
• Est. Priority Date: 10/31/2007
• Status: Expired due to Fees

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: AT&T Intellectual Property II LP (AT&T, Inc.)
• Inventors: Kim, Yeon-Jun, Conkie, Alistair, Ljolje, Andrej, Syrdal, Ann K.
• Primary Examiner(s): MCFADDEN, SUSAN IRIS
• Application Number:
  US11/930,999
• Publication Number:
  US 20090112591A1
• Time in Patent Office: 1,420 Days
• Field of Search: 704/254
• US Class Current: 704/254
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 25/78 Detection of presence or absence of voice signals switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10

Abstract

Systems and methods are provided for recognizing speech in a spoken dialogue system. The method includes receiving input speech having a pre-vocalic consonant or a post-vocalic consonant, generating at least one output lattice that calculates a first score by comparing the input speech to a training model to provide a result and distinguishing between the pre-vocalic consonant and the post-vocalic consonant in the input speech. A second score is calculated by measuring a similarity between the pre-vocalic consonant or the post vocalic consonant in the input speech and the first score. At least one category is determined for the pre-vocalic match or mismatch or the post-vocalic match or mismatch by using the second score and the results of the an automated speech recognition (ASR) system are refined by using the at least one category for the pre-vocalic match or mismatch or the post-vocalic match or mismatch.

Claims

1. The method for recognizing speech, the method comprising:

receiving, via a processor, an input speech having at least one pre-vocalic consonant or at least one post-vocalic consonant;

generating at least one output lattice that calculates a first score by comparing the input speech to a training model to provide a result;

distinguishing between the at least one pre-vocalic consonant and the at least one post-vocalic consonant in the input speech;

calculating a second score by measuring a similarity between the at least one pre-vocalic consonant or the at least one post-vocalic consonant in the input speech and the first score;

determining at least one category for at least one pre-vocalic match or mismatch or at least one post-vocalic match or mismatch by using the second score; and

refining the results of the an automated speech recognition system by using the at least one category for at least one pre-vocalic match or mismatch or at least one post-vocalic match or mismatch.

2. The method of claim 1, wherein if there is a match between the second output lattice and the second score, a word probability is increased.

3. The method of claim 1, wherein at least one output lattice comprises syllabified words.

4. The method of claim 1, wherein if there is a mismatch between the second output lattice and the second score, a word probability is decreased.

5. The method of claim 1, wherein a goodness score is determined by calculating the similarity between the input speech and the training model.

6. The method of claim 1, wherein the automated speech recognition system is trained to distinguish each of the pre-vocalic consonants and the post-vocalic consonants.

7. The method of claim 1, wherein the training model distinguishes between the at least one pre-vocalic consonant and the at least one post-vocalic consonant can be accomplished by using hidden Markov models, support vector machines and neural networks.

8. A system for recognizing speech, the system comprising:

a first module configured, via a processor, to receive an input speech having at least one pre-vocalic consonant or at least one post-vocalic consonant;

a second module configured to generate at least one output lattice that calculates a first score by comparing the input speech to a training model to provide a result;

a third module configured to distinguish between the at least one pre-vocalic consonant and the at least one post-vocalic consonant in the input speech;

a fourth module configured to calculate a second score by measuring a similarity between the at least one pre-vocalic consonant or the at least one post vocalic consonant in the input speech and the first score;

a fifth module configured to determine at least one category for at least one pre-vocalic match or mismatch or at least one post-vocalic match or mismatch by using the second score; and

a sixth module configured to refine the results of the an automated speech recognition system by using the at least one category for at least one pre-vocalic match or mismatch or at least one post-vocalic match or mismatch.

9. The system of claim 8, wherein if there is a match between the second output lattice and the second score, a word probability is increased.

10. The system of claim 8, wherein at least one output lattice comprises syllabified words.

11. The system of claim 8, wherein if there is a mismatch between the second output lattice and the second score, a word probability is decreased.

12. The system of claim 8, wherein a goodness score is determined by calculating the similarity between the input speech and the training model.

13. The system of claim 8, wherein the automated speech recognition system is trained to distinguish each of the pre-vocalic consonants and the post-vocalic consonants.

14. The system of claim 8, wherein the training model distinguishes between the at least one pre-vocalic consonant and the at least one post-vocalic consonant can be accomplished by using hidden Markov models, support vector machines and neural networks.

15. A non-transitory computer-readable medium storing instructions for controlling a computing device to process speech, the instructions comprising:

receiving, via a processor, an input speech having at least one pre-vocalic consonant or at least one post-vocalic consonant;

generating at least one output lattice that calculates a first score by comparing the input speech to a training model to provide a result;

distinguishing between the at least one pre-vocalic consonant and the at least one post-vocalic consonant in the input speech;

calculating a second score by measuring a similarity between the at least one pre-vocalic consonant or the at least one post-vocalic consonant in the input speech and the first score;

determining at least one category for at least one pre-vocalic match or mismatch or at least one post-vocalic match or mismatch by using the second score; and

refining the results of the an automated speech recognition system by using the at least one category for at least one pre-vocalic match or mismatch or at least one post-vocalic match or mismatch.

16. The non-transitory computer-readable medium of claim 15, wherein if there is a match between the second output lattice and the second score, a word probability is increased.

17. The non-transitory computer-readable medium of claim 15, wherein at least one output lattice comprises syllabified words.

18. The non-transitory computer-readable medium of claim 15, wherein if there is a mismatch between the second output lattice and the second score, a word probability is decreased.

19. The non-transitory computer-readable medium of claim 15, wherein a goodness score is determined by calculating the similarity between the input speech and the training model.

20. The non-transitory computer-readable medium of claim 15, wherein the automated speech recognition system is trained to distinguish each of the pre-vocalic consonants and the post-vocalic consonants.

21. The non-transitory computer-readable medium of claim 15, wherein the training model distinguishes between the at least one pre-vocalic consonant and the at least one post-vocalic consonant can be accomplished by using hidden Markov models, support vector machines and neural networks.