Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 284791 14 pages doi 2011 284791 Research Article Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification Leandro D. Vignolo 1 Hugo L. Rufiner 1 Diego H. Milone 1 and John C. Goddard2 1 Research Center for Signals Systems and Computational Intelligence Department of Informatics National University of Litoral CONICET Santa Fe 3000 Argentina 2Departamento de Ingenieria Eléctrica Universidad Autónoma Metropolitana Unidad Iztapalapa Mexico . 09340 Mexico Correspondence should be addressed to Leandro D. Vignolo Received 14 July 2010 Revised 29 October 2010 Accepted 24 December 2010 Academic Editor Raviraj S. Adve Copyright 2011 Leandro D. Vignolo et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Mel-frequency cepstral coefficients have long been the most widely used type of speech representation. They were introduced to incorporate biologically inspired characteristics into artificial speech recognizers. Recently the introduction of new alternatives to the classic mel-scaled filterbank has led to improvements in the performance of phoneme recognition in adverse conditions. In this work we propose a new bioinspired approach for the optimization of the filterbanks in order to find a robust speech representation. Our approach which relies on evolutionary algorithms reduces the number of parameters to optimize by using spline functions to shape the filterbanks. The success rates of a phoneme classifier based on hidden Markov models are used as the fitness measure evaluated over the well-known TIMIT database. The results show that the proposed method is able to find optimized filterbanks for phoneme recognition which significantly increases the .
