INVESTIGADORES
RUFINER Hugo Leonardo
artículos
Título:
Approximated auditory cortical representation of speech with intrinsic robustness
Autor/es:
CÉSAR E. MARTINEZ; JOHN C. GODDARD; DIEGO H. MILONE; HUGO L. RUFINER
Revista:
COMPUTER SPEECH AND LANGUAGE
Editorial:
ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD
Referencias:
Lugar: Amsterdam; Año: 2012 vol. 26 p. 336 - 348
ISSN:
0885-2308
Resumen:
In this work, a first approach to a robust phoneme recognition task by means of a biologically inspired feature extraction method is presented. The proposed technique provides an approximation to the speech signal representation at the auditory cortical level. It is based on an optimal dictionary of atoms, estimated from auditory spectrograms, and the Matching Pursuit algorithm to approximate the cortical activations. This provides a sparse coding with intrinsic noise robustness, which can be therefore exploited when using the system in adverse environments. The recognition task consisted in the classification of a set of 5 easily confused English phonemes, in both clean and noisy conditions. Multilayer perceptrons were trained as classifiers and the performance was compared to other classic and robust parameterizations: the auditory spectrogram, a probabilistic optimum filtering on Mel frequency cepstral coefficients and the perceptual linear prediction coefficients. Results showed a significant improvement in the recognition rate of clean and noisy phonemes by the cortical representation over these other parameterizations.