Creating a new Combined Confidence Measure for ASR-Errors on the Word-Level

We address the problem of localized error detection in Automatic Speech Recognition (ASR) output. Localized error detection seeks to identify which particular words in a user’s utterance have been misrecognized. Identifying misrecognized words permits one to create targeted clarification strategies for spoken dialogue systems, allowing the system to ask clarification questions targeting the particular type of misrecognition, in contrast to the “please repeat/rephrase” strategies used in most current dialogue systems. We present results of machine learning experiments using ASR confidence scores together with prosodic and syntactic features to predict whether 1) an utterance contains an error, and 2) whether a word in a misrecognized utterance is misrecognized. We show that by adding syntactic features to the ASR features when predicting misrecognized utterances the F-measure improves by 8% compared to using ASR features alone. By adding syntactic and prosodic features when predicting misrecognized words F-measure improves by 40%.