Abstract

This paper proposes a probabilistic framework to define and evaluate confidence measures for word recognition. We describe a novel method to combine different knowledge sources and estimate the confidence in a word hypothesis, via a neural network. We also propose a measure of the joint performance of the recognition and confidence systems. The definitions and algorithms are illustrated with results on the Switchboard Corpus. 1. INTRODUCTION In the last few years, a lot of research has been devoted to the development of confidence scores associated with the outputs of automatic speech recognition (ASR) systems. These scores were used mostly to help spot keywords in spontaneous or read texts, and to provide a basis for the rejection of out-of-vocabulary words (e.g. [4-11]). Many other ASR applications could also benefit from knowing the level of confidence in correct recognition. For example, text-dependent speaker recognition systems could put more emphasis on words recognized with h...

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