1st ETRW on Speech Production Modeling: From Control Strategies to Acoustics 4th Speech Production Seminar: Models and Data Autrans, France May 20-24, 1996

A Self-Learning Speech Synthesis System

C. S. Blackburn, Steven J. Young

Cambridge University Engineering Department (CUED), Cambridge, UK

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.

Full Paper

Bibliographic reference.  Blackburn, C. S. / Young, Steven J. (1996): "A self-learning speech synthesis system", In SPM-1996, 225-228.