Automatic speech recognition confidence classifier
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United States of America Patent
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Jun 12, 2018
Issued Date -
Mar 16, 2017
app pub date -
Sep 11, 2015
filing date -
Sep 11, 2015
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Abstract
The described technology provides normalization of speech recognition confidence classifier (CC) scores that maintains the accuracy of acceptance metrics. A speech recognition CC scores quantitatively represents the correctness of decoded utterances in a defined range (e.g., [0,1]). An operating threshold is associated with a confidence classifier, such that utterance recognitions having scores exceeding the operating threshold are deemed acceptable. However, when a speech recognition engine, an acoustic model, and/or other parameters are updated by the platform, the correct-accept (CA) versus false-accept (FA) profile can change such that the application software's operating threshold is no longer valid or as accurate. Normalizing of speech recognition CC scores to map to the same or better CA and/or FA profiles at the previously-set operating thresholds allows preset operating thresholds to remain valid and accurate, even after a speech recognition engine, acoustic model, and/or other parameters are changed.
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