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Designing Optimal Phoneme-Wise Fuzzy Cluster Analysis

A large number of pattern classification algorithms and methodologies have been proposed for the phoneme recognition task during the last decades. The current paper presents a prototype distance-based fuzzy classifier, optimized for the needs of phoneme recognition. This is accomplished by the specially designed objective function and a respective training strategy. Particularly, each phonemic class is represented by a number of arbitrary-shaped clusters which adaptively match the corresponding features space distribution. The formulation of the approach is capable of delivering a variety of related conclusions based on fuzzy logic arithmetic. An overview of the inference capability is presented in combination with performance results for the Greek language.

Authors: Avdelidis, Konstantinos; Dimoulas, Charalampos; Kalliris, George; Papanikolaou, George
Affiliation: Aristotle University of Thessaloniki, Thessaloniki, Greece
AES Convention:128 (May 2010) Paper Number:8137 Import into BibTeX
Subject:Audio Processing—Music and Speech Signal Processing

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