A speech rescoring system is developed on a set of phonetic hypotheses produced by a bottom-up knowledge-based decoder An original method to automatically compute a fuzzy membership function from top-down acoustic rules statistics is compared with a possibilistic measure To aggregate the fuzzy degrees into a phonetic score, a multilayer neural network is trained on the results of all the rules in order to detect how these rules characterize different phonemes and then in order to give a weight to each rule The rescoring performance of top-down rules for fricatives is discussed on an isolated-word speech database of French with 1000 utterances pronounced by five speakers

Rescoring under fuzzy measures with a multilayer neural network in a rule-based speech recognition system

The sound signal processing method involves evaluating the signal distribution value (VM), the word duration value (WM), the similarity value (AM) and the difference value (DM) of the sound signals via a fuzzy technique. This provides a probability value indicating the likelihood that the sound signals contain a given word. The input lines (10,1214,16) can be coupled in pairs to a noise filter (18) and to a word filter (20), with both filters coupled to a combining stage (28) and with a fuzzy output device (36) coupled to an output stage (40).

Method of rejection of unknown words in isolated words speech recognition

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.

/pdf/a-fuzzy-acoustic-phonetic-decoder-for-speech-recognition-4s96zrj3y8.pdf

D. Fournier

Papers

A fuzzy acoustic-phonetic decoder for speech recognition