In this paper, we describe our work on the field of confidence measures for HMM-based speech recognition. Confidence measures are a means of estimating the recognition reliability for single words of the recognizer output. The possible applications of such measures are manifold. We present our experiments with well known approaches and propose some new ones. Particularly, we propose to combine the mere acoustical measures with language model-based ones for continuous speech recognition that involves a stochastic language model. This slightly improves the acoustical measures and preserves their advantage of being computationally very cheap. Experiments are carried out on a German isolated word recognition system and on continuous speech recognition systems for the Resource Management database and the Wall Street Journal WSJ0 task.
Cite as: Willett, D., Worm, A., Neukirchen, C., Rigoll, G. (1998) Confidence measures for HMM-based speech recognition. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0525, doi: 10.21437/ICSLP.1998-816
@inproceedings{willett98b_icslp,
author={Daniel Willett and Andreas Worm and Christoph Neukirchen and Gerhard Rigoll},
title={{Confidence measures for HMM-based speech recognition}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0525},
doi={10.21437/ICSLP.1998-816}
}