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

Author

Ramsay, Gordon

Author_Institution

Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada

Volume

1

fYear

1996

fDate

3-6 Oct 1996

Firstpage

514

Abstract

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

Keywords

acoustic variables measurement; filtering theory; function approximation; maximum likelihood estimation; parameter estimation; speech recognition; speech synthesis; state estimation; statistical analysis; stochastic processes; EM algorithm; acoustic measurements; articulatory measurements; articulatory-acoustic inversion; articulatory-acoustic mapping; artificial simulations; codebooks; expectation maximization; functional approximation; nonlinear filtering approach; parameter estimation; real speech; state estimation; stochastic training; Acoustic measurements; Acoustical engineering; Computational modeling; Extraterrestrial measurements; Filtering algorithms; Speech recognition; Speech synthesis; State estimation; Stochastic processes; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-3555-4

Type

conf

DOI

10.1109/ICSLP.1996.607167

Filename

607167