Recognition Confidence Measures: Detection of Misrecognitions and Out- Of-Vocabulary Words

Abstract
This paper describes and evaluates a new technique for measuring confidence in word strings produced by speech recognition systems. It detects misrecognized and out-of-vocabulary words in spontaneous spoken dialogs. The system uses multiple, diverse knowledge sources including acoustics, semantics, pragmatics and discourse to determine if a word string is misrecognized. When likely misrecognitions are detected, a series of tests distinguishes out-of- vocabulary words from other error sources. The work is part of a larger effort to automatically recognize and understand new words when spoken in a spontaneous spoken dialog. We describe a system that combines newly developed acoustic confidence measures with the semantic, pragmatic and discourse structure knowledge en-bodied in the MINDS-II system. The newly developed acoustic confidence metrics output independent probabilities that a word's recognized correctly along with a measure of how reliably we can estimate if a word is wrong. The acoustic confidence metrics are derived from normalized acoustic recognition scores. The acoustic scores are normalized by estimates of the denominator of Bayes equation. To evaluate the utility of using the acoustic techniques together with higher-level constraints, the preliminary system restricted component interaction. Words with normalized acoustic scores that had a 95% or greater probability of being incorrect were flagged prior to being input to the MINDS-II analysis module.

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