Total: 1
Kinematic articulatory 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 Gaussian Mixture Model (GMM) regression method. In this paper, we explore how additional information (spectrum, articulatory velocity, etc.) affects the performance of the proposed method. The result indicates that acoustic feature (MFCC) is the most effective additional features that improve the reconstruction performance.