Task-independent utterance verification with subword-based minimum verification error training

Abstract

An automated speech recognition system comprises a preprocessor, a speech recognizer, and a task-independent utterance verifier. The task independent utterance verifier employs a first subword acoustic Hidden Markov Model for determining a first likelihood that a speech segment contains a sound corresponding to a speech recognition hypothesis, and a second anti-subword acoustic Hidden Markov Model for determining a second likelihood that a speech segment contains a sound other than one corresponding to the speech recognition hypothesis. In operation, the utterance verifier employs the subword and anti-subword models to produce for each recognized subword in the input speech the first and second likelihoods. The utterance verifier determines a subword verification score as the log of the ratio of the first and second likelihoods. In order to verify larger speech units, the utterance verifier combines the subword verification scores to produce a word/phrase/sentence verification score, and compares that score to a predetermined threshold. The first and second verification-specific HMMs are discriminatively trained using a subword-based minimum verification error training technique.