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A non-linear filtering approach to stochastic training of the articulatory-acoustic mapping using the EM algorithm

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1 Author(s)
G. Ramsay ; Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada

Current techniques for training representations of the articulatory-acoustic mapping from data rely on artificial simulations to provide codebooks of articulatory and acoustic measurements, which are then modelled by simple functional approximations. This paper outlines a stochastic framework for adapting an artificial model to real speech from acoustic measurements alone, using the EM algorithm. It is shown that parameter and state estimation problems for articulatory-acoustic inversion can be solved by adopting a statistical approach based on non-linear filtering

Published in:

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on  (Volume:1 )

Date of Conference:

3-6 Oct 1996