Abstract

This paper describes three experiments in using frame level observation probabilities as the basis for word confidence annotation in an HMM speech recognition system. One experiment is at the word level, one uses word classes, and the other uses phone classes. In each experiment we categorize hypotheses into correct and incorrect categories by aligning a best recognition hypothesis with the known transcript. The confidence of error prediction for each class is a measure of the resolvability between the correct and incorrect histograms. 1. INTRODUCTION Speech recognition systems generally rank order hypotheses by computing scores for utterance hypotheses. These scores are useful for preference ordering the hypotheses, but do not give a good indication of the quality of the recognition or how confident the system is that the decoding is correct. For applications to act on speech input, they must be able to assess the confidence that the input has been decoded correctly. This work combi...

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