A training procedure for verifying string hypotheses in continuous speech recognition

Abstract

A procedure is proposed for verifying the occurrence of string hypotheses produced by a hidden Markov model (HMM) based continuous speech recognizer. Most existing procedures verify word hypotheses through likelihood ratio scoring procedures computed using ad hoc approximations for the density of the alternative hypothesis in the denominator of the likelihood ratio statistic. The discriminative training procedure described in this paper attempts to adjust the parameters of the null hypothesis and the alternate hypothesis models to increase the power of a hypothesis test for utterance verification. The training procedure was evaluated for its ability to detect a twenty word vocabulary in a subset of the Switchboard conversational speech corpus. Experimental results show that the use of this procedure results in significant improvement in the word verification operating characteristic, as well as an improvement in the overall system performance.