In this paper, a general framework of acoustic-phonetic modelling is developed. Context sensitive rules are incorporated into a knowledge-based automatic speech recognition (ASR) system and are assessed with control based on fuzzy decision making. The reliability measure is outlined: a tests collection is run and a confusion matrix is built for each rule. During the recognition procedure the fuzzy set of trained values related to the phonetic unit to be recognized is computed, and its membership function is automatically drawn. Tests were done on an isolated-word speech database of French with 1000 utterances and with 33 rules. The results with a one-speaker low training rate are established via a two-step procedure: a word recognition and a word rejection test bed with five speakers who were never involved during the training.
Cite as: Oppizzi, O., Fournier, D., Gilles, P., Méloni, H. (1996) A fuzzy acoustic-phonetic decoder for speech recognition. Proc. 4th International Conference on Spoken Language Processing (ICSLP 1996), 2270-2273, doi: 10.21437/ICSLP.1996-296
@inproceedings{oppizzi96_icslp,
author={Olivier Oppizzi and David Fournier and Philippe Gilles and Henri Méloni},
title={{A fuzzy acoustic-phonetic decoder for speech recognition}},
year=1996,
booktitle={Proc. 4th International Conference on Spoken Language Processing (ICSLP 1996)},
pages={2270--2273},
doi={10.21437/ICSLP.1996-296}
}