Dynamical System Modelling of Articulator Movement

Simon King, Alan Wrench

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe the modelling of articulatory movements using (hidden) dynamical system models trained on Electro-Magnetic Articulograph (EMA) data. These models can be used for automatic speech recognition and to give insights into articulatory behaviour. They belong to a class of continuous-state Markov models, which we believe can offer improved performance over conventional Hidden Markov Models (HMMs) by better accounting for the continuous nature of the underlying speech production process -- that is, the movements of the articulators. To assess the performance of our models, a simple speech recognition task was used, on which the models show promising results.
Original languageEnglish
Title of host publicationICPhS 99
Subtitle of host publicationProceedings of the XIVth International Congress of Phonetic Sciences
Place of PublicationSan Francisco
PublisherInternational Congress of Phonetic Sciences
Pages2259-2262
Number of pages4
ISBN (Print)9781563968990
Publication statusPublished - 1 Aug 1999

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