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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.
|Title of host publication||ICPhS 99|
|Subtitle of host publication||Proceedings of the XIVth International Congress of Phonetic Sciences|
|Place of Publication||San Francisco|
|Publisher||International Congress of Phonetic Sciences|
|Number of pages||4|
|Publication status||Published - 1 Aug 1999|