A probabilistic approach to confidence estimation and evaluation

In this paper we propose a novel way of estimating confidences for words that are recognized by a speech recognition system, together with a natural methodology for evaluating the overall quality of those confidence estimates. Our approach is based on an interpretation of a confidence as the probability that the corresponding recognized word is correct, and makes use of generalized linear models as a means for combining various predictor scores so as to arrive at confidence estimates. Experimental results using these models are presented based on four different sources of speech data: switchboard, Spanish and Mandarin CallHome, and Wall Street Journal