  Articulatory representation learning is the fundamental research in modeling
neural speech production system. Our previous work has established a deep
paradigm to decompose the articulatory kinematics data into gestures, which
explicitly model the phonological and linguistic structure encoded with human
speech production mechanism, and corresponding gestural scores. We continue
with this line of work by raising two concerns: (1) The articulators are
entangled together in the original algorithm such that some of the articulators
do not leverage effective moving patterns, which limits the interpretability of
both gestures and gestural scores; (2) The EMA data is sparsely sampled from
articulators, which limits the intelligibility of learned representations. In
this work, we propose a novel articulatory representation decomposition
algorithm that takes the advantage of guided factor analysis to derive the
articulatory-specific factors and factor scores. A neural convolutive matrix
factorization algorithm is then employed on the factor scores to derive the new
gestures and gestural scores. We experiment with the rtMRI corpus that captures
the fine-grained vocal tract contours. Both subjective and objective evaluation
results suggest that the newly proposed system delivers the articulatory
representations that are intelligible, generalizable, efficient and
interpretable.
