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

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2 Author(s)
O. Oppizzi ; Lab. Inf. d'Avignon ; R. Quelavoine

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

Published in:

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on  (Volume:3 )

Date of Conference:

21-24 Apr 1997