Evolutionary cepstral coefficients

Artículo

Autoría:

VIGNOLO, LEANDRO DANIEL ; RUFINER, HUGO LEONARDO ; MILONE, DIEGO HUMBERTO ; Goddard, John C.

Fecha:

2011

Editorial y Lugar de Edición:

Elsevier Science

Revista:

APPLIED SOFT COMPUTING, vol. 11 (pp. 3419-3428) Elsevier Science

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.

Palabras Clave

AUTOMATIC SPEECH RECOGNITIONPHONEME CLASSIFICATIONEVOLUTIONARY COMPUTATIONCEPSTRAL COEFFICIENTS

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http://hdl.handle.net/11336/74195