Artículo
Evolutionary cepstral coefficients
Fecha de publicación:
06/2011
Editorial:
Elsevier Science
Revista:
Applied Soft Computing
ISSN:
1568-4946
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Evolutionary algorithms provide flexibility and robustness required to find satisfactory solutions in complex search spaces. This is why they are successfully applied for solving real engineering problems. In this work we propose an algorithm to evolve a robust speech representation, using a dynamic data selection method for reducing the computational cost of the fitness computation while improving the generalisation capabilities. The most commonly used speech representation are the mel-frequency cepstral coefficients, which incorporate biologically inspired characteristics into artificial recognizers. Recent advances have been made with the introduction of alternatives to the classic mel scaled filterbank, improving the phoneme recognition performance in adverse conditions. In order to find an optimal filterbank, filter parameters such as the central and side frequencies are optimised. A hidden Markov model is used as the classifier for the evaluation of the fitness for each individual. Experiments were conducted using real and synthetic phoneme databases, considering different additive noise levels. Classification results show that the method accomplishes the task of finding an optimised filterbank for phoneme recognition, which provides robustness in adverse conditions.
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Articulos(CCT - SANTA FE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Citación
Vignolo, Leandro Daniel; Rufiner, Hugo Leonardo; Milone, Diego Humberto; Goddard, John C.; Evolutionary cepstral coefficients; Elsevier Science; Applied Soft Computing; 11; 4; 6-2011; 3419-3428
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