Efficient decoding and training procedures for utterance verification in continuous speech recognition

Abstract

It is often necessary in speech recognition to include a mechanism for verifying decoded utterances in order to account for incorrectly decoded vocabulary words and utterances corresponding to words or sounds that are not included in a prespecified lexicon. This paper describes an utterance verification procedure for hidden Markov model (HMM) based continuous speech recognition that is based on a likelihood ratio (LR) criterion. There are two important contributions. The first is a search algorithm which directly optimizes a likelihood ratio criterion. This search algorithm is important because it allows decoding to be performed in speech recognition according to the same measure of confidence that is used in hypothesis testing. The second contribution is a corresponding training procedure for estimating model parameters which also directly optimizes the same likelihood ratio criterion. These techniques are applied to spontaneous spoken queries in the context of a movie locator dialog system.