Word-based confidence measures as a guide for stack search in speech recognition

Abstract

The maximum a posteriori hypothesis is treated as the decoded truth in speech recognition. However, since the word recognition accuracy is not 100%, it is desirable to have an independent confidence measure on how good the maximum a posteriori hypothesis is relative to the spoken truth for some applications. Efforts are in progress to develop such confidence measures with the intent of applying them to the assessment of the confidence of whole utterances, rescoring of N-best lists, etc. In this paper, we explore the use of word-based confidence measures to adaptively modify the hypothesis score during searches in continuous speech recognition: specifically, based on the confidence of the current sequence of hypothesized words during the search, the weight of its prediction is changed as a function of the confidence. Experimental results are described for ATIS and SwitchBoard tasks. About 8% relative reduction in word error is obtained for ATIS.