Minimum Bayes-risk automatic speech recognition

Abstract

In this paper we address application of minimum Bayes-risk classifiers to tasks in automatic speech recognition (ASR). Minimum-risk classifiers are useful because they produce hypotheses in an attempt to be optimal under a specified task-dependent performance criterion. While the form of the optimal classifier is well known, its implementation is prohibitively expensive. We present efficient approximations that can be used to implement these procedures. In particular, anA* search over word lattices produced by a conventional ASR system is described. This algorithm is intended to extend the previously proposed N -best list rescoring approximation to minimum-risk classifiers. We provide experimental results showing that both the A*and N -best list rescoring implementations of minimum-risk classifiers yield better recognition accuracy than the commonly used maximum a posteriori probability (MAP) classifier in word transcription and identification of keywords. TheA* implementation is compared to the N -best list rescoring implementation and is found to obtain modest but significant improvements in accuracy at little additional computational cost. Another application of minimum-risk classifiers for the identification of named entities from speech is presented. Only the N -best list rescoring could be implemented for this task and was found to yield better named entity identification performance than the MAP classifier.