Abstract
Natural sounds possess considerable statistical structure. Lewicki (2002, Nature Neurosci. 5(4), 356–363) used independent components analysis (ICA) to reveal the statistical structure of environmental sounds, animal vocalizations, and human speech. Each sound class exhibited distinct statistical properties, but filters that optimally encoded speech closely resembled response properties in the mammalian auditory nerve. This and other analyses of statistical properties of speech examine only global structure without considering systematic variability in different speech sound classes, while acoustic/phonetic analyses of these classes are agnostic to their statistical structure. Here, statistical structure was investigated in principled subdivisions of speech: consonants organized by manner of articulation, and vowels organized by vocal tract configuration. Analyses reveal systematic differences for local statistical structure in speech: statistically optimal filters in ICA were highly diverse for different consonant classes but broadly similar for different vowel classes. While the global statistical structure of speech reflects auditory nerve response properties (Lewicki, 2002), local statistical structure of speech sound classes is well-aligned with cochlear nucleus response properties. Results support theories of efficient coding, in which sensory systems adapt and evolve in order to efficiently capture natural stimulus statistics.
Published Version
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