Utterance verification of keyword strings using word-based minimum verification error (WB-MVE) training

An utterance verification method based on minimum verification error training is presented. In a two-stage process, the recognition hypothesis produced by an HMM-based speech recognizer is verified using 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. This method is evaluated on a connected digit recognition task with a null grammar. The baseline string error rate for this task was 4.85%. At 5% rejection of valid strings, the string error rate decreased to 2.70% using the proposed verification method. The corresponding performance on non-keyword speech was a rejection rate of over 99.0%.