A hybrid physical and statistical dynamic articulatory framework incorporating analysis-by-synthesis for improved phone classification

Abstract

In this paper, we present a dynamic articulatory model for phone classification. The model integrates real articulatory information derived from ElectroMagnetic Articulograph (EMA) data into its inner states. It maps from the articulatory space to the acoustic one using an adapted vocal tract model for each speaker and a physiologically-motivated articulatory synthesis approach. We apply the analysis-by-synthesis paradigm in a statistical fashion. We first present a fast approach for deriving analysis-by-synthesis distortion features. Next, the distortion between the speech synthesized from the articulatory states and the incoming speech signal is used to compute the output observation probabilities of the Hidden Markov Model (HMM) used for classification. Experiments with the novel framework show improvements over baseline in phone classification accuracy.