|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
A SELF-LEARNING SPEECH SYNTHESIS SYSTEM
C.S. Blackburn and S.J. Young
We describe a self-organising pseudo-articulatory speech production model (SPM), and present recent results when training the system on an X-ray microbeam database. The SPM extracts statistics describing articulator positions and curvatures during the production of continuous speech, then applies an explicit co-articulation model to generate synthetic articulator trajectories corresponding to time-aligned phonemic strings. A set of artificial neural networks estimates parameterised speech vectors from the synthetic articulator traces. We present an analysis of the articulatory information in the X-ray microbeam database used, and demonstrate the improvements in articulatory and acoustic modelling accuracy obtained using our co-articulation system.
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