User-specific confidence thresholds for speech recognition
First Claim
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1. A method of automatic speech recognition, comprising the steps of:
- (a) receiving an utterance from a user via a microphone that converts the utterance into a speech signal;
(b) pre-processing the speech signal using a processor to extract acoustic data from the received speech signal;
(c) identifying at least one user-specific characteristic in response to the extracted acoustic data, wherein the at least one user-specific characteristic comprises a plurality of confidence scores associated with failed attempts of the user to store a nametag; and
(d) determining a user-specific confidence threshold responsive to the at least one user-specific characteristic, wherein the determination is carried out by calculating an average of the plurality of confidence scores and setting the user-specific confidence threshold to a value greater than or equal to the calculated average.
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Abstract
A method of automatic speech recognition includes receiving an utterance from a user via a microphone that converts the utterance into a speech signal, pre-processing the speech signal using a processor to extract acoustic data from the received speech signal, and identifying at least one user-specific characteristic in response to the extracted acoustic data. The method also includes determining a user-specific confidence threshold responsive to the at least one user-specific characteristic, and using the user-specific confidence threshold to recognize the utterance received from the user and/or to assess confusability of the utterance with stored vocabulary.
102 Citations
20 Claims
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1. A method of automatic speech recognition, comprising the steps of:
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(a) receiving an utterance from a user via a microphone that converts the utterance into a speech signal; (b) pre-processing the speech signal using a processor to extract acoustic data from the received speech signal; (c) identifying at least one user-specific characteristic in response to the extracted acoustic data, wherein the at least one user-specific characteristic comprises a plurality of confidence scores associated with failed attempts of the user to store a nametag; and (d) determining a user-specific confidence threshold responsive to the at least one user-specific characteristic, wherein the determination is carried out by calculating an average of the plurality of confidence scores and setting the user-specific confidence threshold to a value greater than or equal to the calculated average. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method of automatic speech recognition, comprising the steps of:
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(a) receiving an utterance from a user via a microphone that converts the utterance into a speech signal; (b) pre-processing the speech signal using a processor to extract acoustic data from the received speech signal; (c) identifying at least one user-specific characteristic including pitch and at least one formant in response to the extracted acoustic data; (d) determining a user-specific confidence threshold responsive to the identified at least one user-specific characteristics, wherein the determination comprises using a multiple regression calculation including the identified user-specific pitch and at least one formant and a pitch coefficient and at least one formant coefficient developed from a plurality of development speakers; and (e) decoding the acoustic data based on the user-specific confidence threshold to produce a plurality of hypotheses for the received utterance, including calculating confidence scores for the hypotheses. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A method of automatic speech recognition, comprising the steps of:
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(a) receiving an utterance from a user via a microphone that converts the utterance into a speech signal; (b) pre-processing the speech signal using a processor to extract acoustic data from the received speech signal; (c) identifying at least one user-specific characteristic including a plurality of confidence scores associated with failed attempts of the user to store a nametag; (d) determining a user-specific confidence threshold responsive to the at least one user-specific characteristic; (e) decoding the acoustic data to produce a plurality of hypotheses for the received utterance, including calculating confidence scores for the hypotheses; and (f) post-processing the plurality of hypotheses, including using the user-specific confidence threshold to assess confusability of the utterance with stored vocabulary, wherein the user-specific confidence threshold is a confusability confidence threshold. - View Dependent Claims (18, 19, 20)
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Specification