Estimating the position of mistracked coil of EMA data using GMM-based methods

Abstract

Kinematic arti dilatory data are important for researches of speech production, articulatory speech synthesis, robust speech recognition, and speech inversion. Electromagnetic Articulograph (EMA) is a widely used instrument for collecting kinematic articulatory data. However, in EMA experiment, one or more coils attached to articulators are possible to be mistracked due to various reasons. To make full use of the EMA data, we attempt to reconstruct the location of mistracked coils with the methods based on Gaussian Mixture Model (GMM). These methods approximate the probability density function of the positions for the concerned coil given the positions of the other coils, then elaborating regression functions by using Minimum Mean Square Error (MMSE) and Maximum Likelihood (ML) methods. The results indicate that: i.) The positions of mistracked coils could be reconstructed from the positions of correctly tracked coils with the RMSE between 1mm and 1.5mm; ii.) The performance can be further improved by incorporating the velocity information in most cases.