Audiovisual-to-Articulatory Speech Inversion Using HMMs
We address the problem of audiovisual speech inversion, namely recovering the vocal tract's geometry from auditory and visual speech cues. We approach the problem in a statistical framework, combining ideas from multistream Hidden Markov Models and canonical correlation analysis, and demonstrate effective estimation of the trajectories followed by certain points of interest in the speech production system. Our experiments show that exploiting both audio and visual modalities clearly improves performance relative to either audio-only or visual-only estimation. We report experiments on the QSMT database which contains audio, video, and electromagnetic articulography data recorded in parallel.