Discriminative utterance verification using multiple confidence measures

This paper proposes an utterance veri cation system for hidden Markov model (HMM) based automatic speech recognition systems. A veri cation objective function, based on a multi-layer-perceptron (MLP), is adopted which combines con dence measures from both the recognition and veri cation models. Discriminative minimum veri cation error training is applied for optimizing the parameters of the MLP and the veri cation models. Our proposed system provides a framework for combining different knowledge sources for utterance veri cation using an objective function that is consistently applied during both training and testing. Experimental results on telephone-based connected digits are presented.