Abstract

Speech prosody is manifest in the acoustic signal through the modulation of pitch, loudness, duration, and source characteristics (voice quality), which combine to encode the prosodic structure of an utterance. Prosodic structure defines the location of prominent words and syllables, and the grouping of words into phonological phrases. Prosodic structure, in turn, relates the phonological form of an utterance to its morphological, syntactic, semantic, and pragmatic context. The listener’s task in comprehending speech includes decoding prosodic structure to aid in identifying the morphological, syntactic, semantic, and pragmatic contexts that comprise the meaning of the utterance. The research reported in this dissertation focuses on acoustic and perceptual evidence for prosody in spoken language, and the relationship between prosodic structure and higher levels of linguistic organization. The study adopts a computational approach that employs natural language processing tools, machine learning algorithms, and speech and signal pro-cessing techniques to investigate prosody in speech corpus data. In this study, I show that prosodic features of an utterance can be reliably predicted from a set of features that en-

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