We present a computational model of human vocalization which aims at learning the articulatory mechanisms which produce spoken phonemes. It uses a set of fuzzy rules and genetic optimization. The former represents the relationships between places of articulations and speech acoustic parameters, while the latter computes the degrees of membership of the places of articulation. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the articulatory features. Subjective listening tests of sentences artificially generated from the articulatory description resulted in an average phonetic accuracy of about 76 %. Through the analysis of a large amount of natural speech, the algorithm can be used to learn the places of articulation of all phonemes.

Genetic-fuzzy optimization algorithm for adaptive learning of human vocalization in robotics

MUMOLO, ENZO;M. NOLICH;
2005

Abstract

We present a computational model of human vocalization which aims at learning the articulatory mechanisms which produce spoken phonemes. It uses a set of fuzzy rules and genetic optimization. The former represents the relationships between places of articulations and speech acoustic parameters, while the latter computes the degrees of membership of the places of articulation. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the articulatory features. Subjective listening tests of sentences artificially generated from the articulatory description resulted in an average phonetic accuracy of about 76 %. Through the analysis of a large amount of natural speech, the algorithm can be used to learn the places of articulation of all phonemes.
978-3-540-25396-9
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/1696668
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