Neural-network based measures of confidence for word recognition
Mitchel Weintraub, Françoise Beaufays, Ze’ev Rivlin, Yochai Konig, Andreas Stolcke · 2002
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.