Unsupervised Acoustic-to-Articulatory Inversion with Variable Vocal Tract Anatomy

Acoustic and articulatory variability across speakers has al-ways limited the generalization performance of acoustic-to-articulatory inversion (AAI) methods. Speaker-independent AAI (SI-AAI) methods generally focus on the transformation of acoustic features, but rarely consider the direct matching in the articulatory space. Unsupervised AAI methods have the potential of better generalization ability but typically use a fixed mor-phological setting of a physical articulatory synthesizer even for different speakers, which may cause nonnegligible articulatory compensation. In this paper, we propose to jointly estimate articulatory movements and vocal tract anatomy during the inversion of speech. An unsupervised AAI framework is employed, where estimated vocal tract anatomy is used to set the configuration of a physical articulatory synthesizer, which in turn is driven by estimated articulation movements to imitate a given speech. Experiments show that the estimation of vocal tract anatomy can bring both acoustic and articulatory benefits. Acoustically, the reconstruction quality is higher; articulatorily, the estimated articulatory movement trajectories better match the measured ones. Moreover, the estimated anatomy parameters show clear clusterings by speakers, indicating successful decoupling of speaker characteristics and linguistic content.