Non-linear score scrunching for more efficient comparison of hypotheses

• US 20040193412A1
• Filed: 03/18/2003
• Published: 09/30/2004
• Est. Priority Date: 03/18/2003
• Status: Abandoned Application

• Current Assignee: Aurilab LLC
• Original Assignee: Aurilab LLC
• Inventors: Baker, James K.
• Application Number:
  US10/389,934
• Publication Number:
  US 20040193412A1
• Time in Patent Office: Days
• US Class Current: 704/240
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/10 using distance or distortion measures between unknown speech and reference templates

Abstract

A speech recognition method, system, and program product, the method comprising in one embodiment: obtaining a frame match score for each of a plurality of different speech elements for a frame; obtaining a scrunched score for each of a plurality of the frame match scores for the frame, wherein a scrunched score means applying a non-linear transformation to each of the frame match scores so that frame match score differences among relatively good competing frame matches are reduced while the score differences between good frame matches and the poor frame matches is substantially maintained or increased, wherein a relatively good frame match score is determined based on a criterion; for each of a plurality of hypotheses, accumulating the scrunched scores for frames of the hypothesis to obtain a hypothesis scrunched score for the hypothesis; selecting a plurality of hypotheses with better hypothesis scrunched scores as compared to the accumulated scrunched scores for other hypotheses; for each of the selected hypotheses, determining a non-scrunched score for that hypothesis; and selecting the best hypothesis from among the selected plurality of hypotheses based at least in part on the non-scrunched scores.

Claims

1. A speech recognition method, comprising:

obtaining a frame match score for each of a plurality of different speech elements for a frame;

obtaining a scrunched score for each of a plurality of the frame match scores for the frame, wherein a scrunched score means applying a non-linear transformation to each of the frame match scores so that frame match score differences among relatively good competing frame matches are reduced while the score differences between good frame matches and the poor frame matches is substantially maintained or increased, wherein a relatively good frame match score is determined based on a criterion;

for each of a plurality of hypotheses, accumulating the scrunched scores for frames of the hypothesis to obtain a hypothesis scrunched score for the hypothesis;

selecting a plurality of hypotheses with better hypothesis scrunched scores as compared to the accumulated scrunched scores for other hypotheses;

for each of the selected hypotheses, determining a non-scrunched score for that hypothesis; and

selecting the best hypothesis from among the selected plurality of hypotheses based at least in part on the non-scrunched scores.

2. The method as defined in claim 1, further comprising performing a branch-and-bound search based on the hypothesis scrunched scores.

3. The method as defined in claim 1, further comprising performing a priority queue search wherein the priority queue is sorted based on the hypothesis scrunched scores.

4. The method as defined in claim 1, wherein the criterion for determining a good match frame score is whether the frame match score is better than a predetermined value.

5. The method as defined in claim 1, wherein the criterion for determining a good match frame scores is to determine if a difference between a best frame score for that frame and another match frame score for that frame is less than a predetermined value.

6. A speech recognition method, comprising:

obtaining a first table of hypothesis speech element match scores on a frame-by-frame basis;

obtaining a second hypotheses table of scrunched scores processed by applying a non-linear transformation to each of a set of different hypothesis speech element frame match scores so that frame match score differences among relatively good competing frame matches are reduced while the score differences between good frame matches and the poor frame matches is substantially maintained or increased, wherein a relatively good frame match score is determined based on a criterion;

for each of a plurality of hypotheses, accumulating the scrunched scores from the second table for frames of the hypothesis to obtain a hypothesis scrunched score for the hypothesis;

selecting a plurality of hypotheses with better hypothesis scrunched scores as compared to the accumulated scrunched scores for other hypotheses;

for each of the selected plurality of hypotheses, accumulating the frame match scores therefor on a frame-by-frame basis from the first table; and

selecting a best hypothesis from among the selected plurality of hypotheses based at least in part on the accumulated match scores.

7. The method as defined in claim 6, further comprising generating the second hypotheses table by performing the non-linear transformation on frame match scores in the first hypotheses table.

8. A program product for speech recognition, comprising machine-readable program code for causing, when executed, a machine to perform the following method steps of:

obtaining a frame match score for each of a plurality of different speech elements for a frame;

obtaining a scrunched score for each of a plurality of the frame match scores for the frame, wherein a scrunched score means applying a non-linear transformation to each of the frame match-scores so that frame match score differences among relatively good competing frame matches are reduced while the score differences between good frame matches and the poor frame matches is substantially maintained or increased, wherein a relatively good frame match score is determined based on a criterion;

for each of a plurality of hypotheses, accumulating the scrunched scores for frames of the hypothesis to obtain a hypothesis scrunched score for the hypothesis;

selecting a plurality of hypotheses with better hypothesis scrunched scores as compared to the accumulated scrunched scores for other hypotheses;

for each of the selected hypotheses, determining a non-scrunched score for that hypothesis; and

selecting the best hypothesis from among the selected plurality of hypotheses based at least in part on the non-scrunched scores.

9. The program product as defined in claim 8, further comprising program code for performing a branch-and-bound search using the hypothesis scrunched scores.

10. The program product as defined in claim 8, further comprising program code for performing a priority queue search wherein the priority queue is sorted based on the hypothesis scrunched scores.

11. The program product as defined in claim 8, wherein the criterion for determining a good match frame score is whether the frame match score is better than a predetermined value.

12. The program product as defined in claim 8, wherein the criterion for determining a good match frame scores is to determine if a difference between a best frame score for that frame and another match frame score for that frame is less than a predetermined value.

13. A speech recognition system, comprising:

a component for obtaining a frame match score for each of a plurality of different speech elements for a frame;

a component for obtaining a scrunched score for each of a plurality of the frame match scores for the frame, wherein a scrunched score means applying a non-linear transformation to each of the frame match scores so that frame match score differences among relatively good competing frame matches are reduced while the score differences between good frame matches and the poor frame matches is substantially maintained or increased, wherein a relatively good frame match score is determined based on a criterion;

a component for, for each of a plurality of hypotheses, accumulating the scrunched scores for frames of the hypothesis to obtain a hypothesis scrunched score for the hypothesis;

a component for selecting a plurality of hypotheses with better hypothesis scrunched scores as compared to the accumulated scrunched scores for other hypotheses;

a component for, for each of the selected hypotheses, determining a non-scrunched score for that hypothesis; and

a component for selecting the best hypothesis from among the selected plurality of hypotheses based at least in part on the non-scrunched scores.

14. A speech recognition system, comprising:

means for obtaining a frame match score for each of a plurality of different speech elements for a frame;

means for obtaining a scrunched score for each of a plurality of the frame match scores for the frame, wherein a scrunched score means applying a non-linear transformation to each of the frame match scores so that frame match score differences among relatively good competing frame matches are reduced while the score differences between good frame matches and the poor frame matches is substantially maintained or increased, wherein a relatively good frame match score is determined based on a criterion;

means for, for each of a plurality of hypotheses, accumulating the scrunched scores for frames of the hypothesis to obtain a hypothesis scrunched score for the hypothesis;

means for selecting a plurality of hypotheses with better hypothesis scrunched scores as compared to the accumulated scrunched scores for other hypotheses;

means for, for each of the selected hypotheses, determining a non-scrunched score for that hypothesis; and

means for selecting the best hypothesis from among the selected plurality of hypotheses based at least in part on the non-scrunched scores.