Data-driven Model Construction for Continuous Speech Recognition Using Overlapping Articulatory Features

A new, data-driven approach to deriving overlapping

articulatory-feature based HMMs for speech recognition is

presented in this paper. This approach uses speech data from

University of Wisconsin's Microbeam X-ray Speech Production

Database. Regression tree models were created for constructing

HMMs. Use of actual articulatory data improves upon our

previous rule-based feature overlapping system. The regression

trees allow construction of the HMM topology for an arbitrary

utterance given its phonetic transcription and some prosodic

information. Experimental results in ASR show preliminary

success of this approach.

2000-deng-icslpb.pdf
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In  Proc. of the Int. Conf. on Spoken Language Processing

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