A modular and hybrid connectionist system for speaker identification.
This paper presents and evaluates a modular/hybrid connectionist system for speaker identification. Modularity has emerged as a powerful technique for reducing the complexity of connectionist systems, and allowing a priori knowledge to be incorporated into their design. Text-independent speaker identification is an inherently complex task where the amount of training data is often limited. It thus provides an ideal domain to test the validity of the modular/hybrid connectionist approach. To achieve such identification, we develop, in this paper, an architecture based upon the cooperation of several connectionist modules, and a Hidden Markov Model module. When tested on a population of 102 speakers extracted from the DARPA-TIMIT database, perfect identification was obtained.
Bennani
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C.N.R.S., L.I.P.N. URA-1507, University of Paris-Nord, Villetaneuse, France.
D016208:Databases, Factual; D005103:Expert Systems; D005602:France; D006801:Humans; D007206:Individuality; D007802:Language; D008037:Linguistics / Q000295:instrumentation*; D008390:Markov Chains; D013060:Speech; D014831:Voice