|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
PSEUDO-ARTICULATORY SPEECH SYNTHESIS FOR RECOGNITION USING AUTOMATIC FEATURE EXTRACTION FROM X-RAY DATA
C.S. Blackburn and S.J. Young
We describe a self-organising pseudo-articulatory speech production model (SPM) trained on an X-ray microbeam database, and present results when using the SPM within a speech recognition framework. Given a time-aligned phonemic string, the system uses an explicit statistical model of co-articulation to generate pseudo-articulator trajectories. From these, parametrised speech vectors are synthesised using a set of artificial neural networks (ANNs). We present an analysis of the articulatory information in the database used, and demonstrate the improvements in articulatory modelling accuracy obtained using our co-articulation system. Finally, we give results when using the SPM to re-score N-best utterance transcription lists as produced by the CUED HTK Hidden Markov Model (HMM) speech recognition system. Relative reductions of 18\% in the phoneme error rate and 15\% in the word error rate are achieved.
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