Method and apparatus for discriminative utterance verification using

Abstract

A multiple confidence measures subsystem of an automated speech recognition system allows otherwise independent confidence measures to be integrated and used for both training and testing on a consistent basis. Speech to be recognized is input to a speech recognizer and a recognition verifier of the multiple confidence measures subsystem. The speech recognizer generates one or more confidence measures. The speech recognizer preferably generates a misclassification error (MCE) distance as one of the confidence measures. The recognized speech output by the speech recognizer is input to the recognition verifier, which outputs one or more confidence measures. The recognition verifier preferably outputs a misverification error (MVE) distance as one of the confidence measures. The confidence measures output by the speech recognizer and the recognition verifier are normalized and then input to an integrator. The integrator integrates the various confidence measures during both a training phase for the hidden Markov models implemented in the speech recognizer and the recognition verifier and during testing of the input speech. The integrator is preferably implemented using a multi-layer perceptron (MLP). The output of the integrator, rather than the recognition verifier, determines whether the recognized utterance hypothesis generated by the speech recognizer should be accepted or rejected.