The fine-grained statistical structure of speech is congruent with nonlinear peripheral auditory processing

Abstract

Efficient coding of sensory signals takes advantage of statistical regularities in sensory data. Cochlear filter in mammals are known to reflect the overall statistical structure of speech, in line with the hypothesis that low-level sensory processing provides efficient codes for information contained in natural stimuli. Recently, some efforts have been made to describe this correspondence in more detail. The study of the statistical structure of speech over different acoustic classes demonstrates that frequency selectivity should not be fixed to achieve maximum efficiency. On the other hand, cochlear signal processing is nonlinear as frequency selectivity decreases with sound intensity level. Both effects are greater in the high frequencies. In the present study, these two facts are shown to be consistent in the case of a parametric method based on Gabor dictionaries (Gaussian-modulated sinusoids) and in a simplified setting. A model with fewer constrains is also introduced for future experiments to validate this hypothesis in a more general context.