288 pp. per issue
6 x 9, illustrated
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Neural Computation

July 1995, Vol. 7, No. 4, Pages 791-798
(doi: 10.1162/neco.1995.7.4.791)
© 1995 Massachusetts Institute of Technology
A Modular and Hybrid Connectionist System for Speaker Identification
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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.