Google Inc.

Yanwei Pan

Zhiyun Lu

Liangliang Cao

Chao Zhang

Thibault Doutre

A method includes obtaining a set of training samples, wherein each training sample includes a corresponding sequence of speech segments corresponding to a training utterance and a corresponding sequence of ground-truth transcriptions for the sequence of speech segments, and wherein each ground-truth transcription includes a start time and an end time of a corresponding speech segment. For each training sample in the set of training samples, the method includes processing, using a speech recognition model, the corresponding sequence of speech segments to obtain one or more speech recognition hypotheses for the training utterance; and, for each speech recognition hypothesis obtained for the training utterance, identifying a respective number of word errors relative to the corresponding sequence of ground-truth transcriptions. The method trains the speech recognition model to minimize word error rate based on the respective number of word errors identified for each speech recognition hypothesis obtained for the training utterance.

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Yanwei Pan