2019
A Factorial Deep Markov Model for Unsupervised Disentangled Representation Learning from Speech
ICASSP 2019accepted
We present the Factorial Deep Markov Model (FDMM) for representation learning of speech. The FDMM learns disentangled, interpretable and lower dimensional latent representations from speech without supervision. We use a static and dynamic latent variable to exploit the fact that information in a spe…