About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICASSP 1997
Conference paper
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 it to assessment of 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 search in continuous speech recognition: specifically, based on the confidence of the current sequence of hypothesized words during 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.