System and method for estimating the reliability of alternate speech recognition hypotheses in real time
First Claim
1. A method comprising:
- receiving an N-best list of speech recognition hypotheses from a speech utterance, wherein the N-best list of speech recognition hypotheses comprises words recognized from the speech utterance;
receiving an acoustic score of each word in the N-best list of speech recognition hypotheses;
receiving a count indicating a number of words associated with each hypothesis in the speech recognition hypotheses;
receiving an indication of problematic words in the each hypothesis in the N-best list of speech recognition hypotheses, wherein the indication is determined by a reliability estimator;
determining, via a processor and based on a feature set evaluated by an algorithm, a first probability of correctness for the each hypothesis in the N-best list of speech recognition hypotheses, the feature set evaluated by the algorithm comprising the count, the acoustic score, and the indication of problematic words;
determining, via the processor, a second probability that the N-best list of speech recognition hypotheses does not contain a correct hypothesis using the reliability estimator; and
using the first probability and the second probability in a spoken dialog.
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Accused Products
Abstract
Disclosed herein are systems, methods, and computer-readable storage media for estimating reliability of alternate speech recognition hypotheses. A system configured to practice the method receives an N-best list of speech recognition hypotheses and features describing the N-best list, determines a first probability of correctness for each hypothesis in the N-best list based on the received features, determines a second probability that the N-best list does not contain a correct hypothesis, and uses the first probability and the second probability in a spoken dialog. The features can describe properties of at least one of a lattice, a word confusion network, and a garbage model. In one aspect, the N-best lists are not reordered according to reranking scores. The determination of the first probability of correctness can include a first stage of training a probabilistic model and a second stage of distributing mass over items in a tail of the N-best list.
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Citations
20 Claims
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1. A method comprising:
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receiving an N-best list of speech recognition hypotheses from a speech utterance, wherein the N-best list of speech recognition hypotheses comprises words recognized from the speech utterance; receiving an acoustic score of each word in the N-best list of speech recognition hypotheses; receiving a count indicating a number of words associated with each hypothesis in the speech recognition hypotheses; receiving an indication of problematic words in the each hypothesis in the N-best list of speech recognition hypotheses, wherein the indication is determined by a reliability estimator; determining, via a processor and based on a feature set evaluated by an algorithm, a first probability of correctness for the each hypothesis in the N-best list of speech recognition hypotheses, the feature set evaluated by the algorithm comprising the count, the acoustic score, and the indication of problematic words; determining, via the processor, a second probability that the N-best list of speech recognition hypotheses does not contain a correct hypothesis using the reliability estimator; and using the first probability and the second probability in a spoken dialog. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, result in the processor performing operations comprising; receiving an N-best list of speech recognition hypotheses from a speech utterance, wherein the N-best list of speech recognition hypotheses comprises words recognized from the speech utterance; receiving an acoustic score of each word in the N-best list of speech recognition hypotheses; receiving a count indicating a number of words associated with each hypothesis in the speech recognition hypotheses; receiving an indication of problematic words in the each hypothesis in the N-best list of speech recognition hypotheses, wherein the indication is determined by a reliability estimator; determining, based on a feature set evaluated by an algorithm, a first probability of correctness for the each hypothesis in the N-best list of speech recognition hypotheses, the feature set evaluated by the algorithm comprising the count, the acoustic score, and the indication of problematic words; determining a second probability that the N-best list of speech recognition hypotheses does not contain a correct hypothesis using the reliability estimator; and using the first probability and the second probability in a spoken dialog. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A computer-readable storage medium having instructions stored which, when executed by a computing device, cause the computing device perform operations comprising:
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receiving an N-best list of speech recognition hypotheses from a speech utterance, wherein the N-best list of speech recognition hypotheses comprises words recognized from the speech utterance; receiving an acoustic score of each word in the N-best list of speech recognition hypotheses; receiving a count indicating a number of words associated with each hypothesis in the speech recognition hypotheses; receiving an indication of problematic words in the each hypothesis in the N-best list of speech recognition hypotheses, wherein the indication is determined by a reliability estimator; determining, based on a feature set evaluated by an algorithm, a first probability of correctness for the each hypothesis in the N-best list of speech recognition hypotheses, the feature set evaluated by the algorithm comprising the count, the acoustic score, and the indication of problematic words; determining a second probability that the N-best list of speech recognition hypotheses does not contain a correct hypothesis using the reliability estimator; and using the first probability and the second probability in a spoken dialog. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification