Abstract

Natural, hands-free interaction with computers is currently one of the great unfulfilled promises of automatic speech recognition (ASR), in part because ASR systems cannot reliably recognize speech under everyday, reverberant conditions that pose no problems for most human listeners. The specific properties of the auditory representation of speech likely contribute to reliable human speech recognition under such conditions. This dissertation explores the use of perceptually inspired signal-processing strategies -- critical-band-like frequency analysis, an emphasis of slow changes in the spectral structure of the speech signal, adaptation, integration of phonetic information over syllabic durations, and use of multiple signal representations for...

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