Year: 1999
Type: article
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. 1. INTRODUCTION Our investigation of dynamical system models is motivated both by an interest in new models for speech recognition, and by the availability of new articulatory measurement data. For speech recognition, we are investigating alternatives to Hidden Markov Models (HMMs) in which speech is generally seen as a sequ...
Authors: Simon King, Alan Wrench
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1498899745