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Abstract / Description of output
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 language | English |
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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 |
Pages | 2259-2262 |
Number of pages | 4 |
ISBN (Print) | 9781563968990 |
Publication status | Published - 1 Aug 1999 |
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