A fuzzy acoustic-phonetic decoder for speech recognition
Olivier Oppizzi, D. Fournier, P. Gilles, Henri Méloni · 2002
A general framework for 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. A reliability measure is outlined, a test 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 33 rules. The results with a one-speaker low training rate are established via a two-step procedure: word recognition and a word-rejection testbed with five speakers who were never involved during the training.