Correcting recognition errors via discriminative utterance verification

Utterance verification (UV) is a process by which the output of a speech recognizer is verified to determine if the input speech actually includes the recognized keyword(s). The output of the speech verifier is a binary decision to accept or reject the recognized utterance based on a UV confidence score. In this paper, we extend the notion of utterance verification to not only detect errors but also to selectively correct them. We perform error correction by flipping the hypotheses produced by an N-best recognizer in cases when the top candidate has a UV confidence score that is lower than that of the next candidate. We propose two measures for computing confidence scores and investigate the use of a hybrid confidence measure that combines the two measures into a single score. Using this hybrid confidence measure and an N-best algorithm, we obtained an 11% improvement in word-error rate on a connected digit recognition task. This improvement was achieved while still maintaining reliable detection of non-keyword speech and misrecognitions