Bioinspired sparse spectro-temporal representation of speech for robust classification

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.

► We address the problem of signal representation in the broad field of robust speech recognition.
► We propose a biologically inspired method to obtain the feature vectors by modeling the auditory cortical response to stimuli.
► The method calculates an optimal dictionary of atoms from the auditory spectrograms.
► The Matching Pursuit algorithm selects the more representative coefficients, thresholding the noisy components.
► For phoneme classification, our method outperforms the standard parameterizations used in automatic speech recognition.