Non-linear score scrunching for more efficient comparison of hypotheses
First Claim
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.
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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.
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Citations
14 Claims
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1. A speech recognition method, comprising:
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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. - View Dependent Claims (2, 3, 4, 5)
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6. A speech recognition method, comprising:
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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. - View Dependent Claims (7)
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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:
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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. - View Dependent Claims (9, 10, 11, 12)
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13. A speech recognition system, comprising:
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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.
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14. A speech recognition system, comprising:
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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.
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Specification