Active labeling for spoken language understanding
First Claim
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1. A spoken language understanding system comprising:
- a processor;
a first module configured to control the processor to classify a set of labeled candidate utterances based on a previously trained classifier and generate classification types for each candidate utterance, each classification type having a respective confidence score;
a second module configured to control the processor to sort the candidate utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and
a third module configured to control the processor to recheck candidate utterances according to when the Kullback-Liebler divergence is greater than a threshold.
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Abstract
A spoken language understanding method and system are provided. The method includes classifying a set of labeled candidate utterances based on a previously trained classifier, generating classification types for each candidate utterance, receiving confidence scores for the classification types from the trained classifier, sorting the classified utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, and rechecking candidate utterances according to the analysis. The system includes modules configured to control a processor in the system to perform the steps of the method.
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Citations
14 Claims
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1. A spoken language understanding system comprising:
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a processor; a first module configured to control the processor to classify a set of labeled candidate utterances based on a previously trained classifier and generate classification types for each candidate utterance, each classification type having a respective confidence score; a second module configured to control the processor to sort the candidate utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and a third module configured to control the processor to recheck candidate utterances according to when the Kullback-Liebler divergence is greater than a threshold. - View Dependent Claims (2, 3, 4, 5)
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6. A tangible computer-readable medium storing instructions for controlling a computer device, as part of a spoken language understanding system, to perform the steps of:
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classifying a set of labeled candidate utterances based on a previously trained classifier; generating classification types for each candidate utterance, each classification type having a respective confidence score; sorting the candidate utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and rechecking the candidate utterances that do not match the label of the respective candidate utterance when the Kullback-Liebler divergence is greater than a threshold. - View Dependent Claims (7, 8, 9)
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10. A computer-implemented method for automatic speech recognition that extracts words from user speech, the system comprising:
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classifying, via a processor, a set of labeled candidate utterances based on a previously trained classifier; generating, via a processor, classification types for each candidate utterance; receiving confidence scores for the classification types from the trained classifier; sorting the classified utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and rechecking candidate utterances when the Kullback-Liebler divergence is greater than a threshold. - View Dependent Claims (11, 12, 13, 14)
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Specification