Cross-speaker Acoustic-to-Articulatory Inversion using Phone-based Trajectory HMM for Pronunciation Training

The article presents a statistical mapping approach for crossspeaker acoustic-to-articulatory inversion. The goal is to estimate the most likely articulatory trajectories for a reference speaker from the speech audio signal of another speaker. This approach is developed in the framework of our system of visual articulatory feedback developed for computer-assisted pronunciation training applications (CAPT). The proposed technique is based on the joint modeling of articulatory and acoustic features, for each phonetic class, using full-covariance trajectory HMM. The acoustic-to-articulatory inversion is achieved in 2 steps: 1) finding the most likely HMM state sequence from the acoustic observations; 2) inferring the articulatory trajectories from both the decoded state sequence and the acoustic observations. The problem of speaker adaptation is addressed using a voice conversion approach, based on trajectory GMM.