14th International Congress of Phonetic Sciences (ICPhS-14)
San Francisco, CA, USA
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.
Bibliographic reference. King, Simon / Wrench, Alan A. (1999): "Dynamical system modelling of articulator movement", In ICPhS-14, 2259-2262.