  In this paper, we present a multimodal and dynamical VAE (MDVAE) applied to
unsupervised audio-visual speech representation learning. The latent space is
structured to dissociate the latent dynamical factors that are shared between
the modalities from those that are specific to each modality. A static latent
variable is also introduced to encode the information that is constant over
time within an audiovisual speech sequence. The model is trained in an
unsupervised manner on an audiovisual emotional speech dataset, in two stages.
In the first stage, a vector quantized VAE (VQ-VAE) is learned independently
for each modality, without temporal modeling. The second stage consists in
learning the MDVAE model on the intermediate representation of the VQ-VAEs
before quantization. The disentanglement between static versus dynamical and
modality-specific versus modality-common information occurs during this second
training stage. Extensive experiments are conducted to investigate how
audiovisual speech latent factors are encoded in the latent space of MDVAE.
These experiments include manipulating audiovisual speech, audiovisual facial
image denoising, and audiovisual speech emotion recognition. The results show
that MDVAE effectively combines the audio and visual information in its latent
space. They also show that the learned static representation of audiovisual
speech can be used for emotion recognition with few labeled data, and with
better accuracy compared with unimodal baselines and a state-of-the-art
supervised model based on an audiovisual transformer architecture.
