  Most of the research on data-driven speech representation learning has
focused on raw audios in an end-to-end manner, paying little attention to their
internal phonological or gestural structure. This work, investigating the
speech representations derived from articulatory kinematics signals, uses a
neural implementation of convolutive sparse matrix factorization to decompose
the articulatory data into interpretable gestures and gestural scores. By
applying sparse constraints, the gestural scores leverage the discrete
combinatorial properties of phonological gestures. Phoneme recognition
experiments were additionally performed to show that gestural scores indeed
code phonological information successfully. The proposed work thus makes a
bridge between articulatory phonology and deep neural networks to leverage
informative, intelligible, interpretable,and efficient speech representations.
