  It is increasingly considered that human speech perception and production
both rely on articulatory representations. In this paper, we investigate
whether this type of representation could improve the performances of a deep
generative model (here a variational autoencoder) trained to encode and decode
acoustic speech features. First we develop an articulatory model able to
associate articulatory parameters describing the jaw, tongue, lips and velum
configurations with vocal tract shapes and spectral features. Then we
incorporate these articulatory parameters into a variational autoencoder
applied on spectral features by using a regularization technique that
constraints part of the latent space to follow articulatory trajectories. We
show that this articulatory constraint improves model training by decreasing
time to convergence and reconstruction loss at convergence, and yields better
performance in a speech denoising task.
