Verifying and correcting recognition string hypotheses using 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 by presenting an utterance verification method that will be utilized to perform three tasks. (1) detect non-keyword strings (false alarms), (2) detect keyword substitution errors, and (3) selectively correct substitution errors when N-best string hypotheses are available. The utterance verification method presented here employs a set of verification-specific models that are independent of the models used in the recognition process. The verification models are trained using a discriminative training procedure that seeks to minimize the verification error by simultaneously maximizing the rejection of non-keywords and misrecognized keywords while minimizing the rejection of correctly recognized keywords. The error correction is performed by reordering the hypotheses produced by an N-best recognizer based on a UV confidence score. (C) 1997 Elsevier Science B.V.