This course surveys advanced topics in automatic speech recognition and auditory representation of speech signals, especially as they relate to speech perception. The course is constructed around a thorough introduction to state-of-the-art techniques in automatic speech recognition and relates these to perspectives obtained from perceptual and neurophysiological research. The course begins with the necessary fundamentals in digital signal processing and statistical pattern recognition, then discusses the major techniques in automatic speech recognition, including neural networks, hidden markov models, and dynamic programming. It explores the relation of these techniques to neurophysiological processing and psycholinguistic data, and evaluates neural models of auditory processing and speech perception. Modeling techniques, including parameter optimization and goodness-of-fit tests, are covered.