Title: Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data,
Abstract: We present a factorized hierarchical variational autoencoder, which learns
disentangled and interpretable representations from sequential data without
supervision. Specifically, we exploit the multi-scale nature of information in
sequential data by formulating it explicitly within a factorized hierarchical
graphical model that imposes sequence-dependent priors and sequence-independent
priors to different sets of latent variables. The model is evaluated on two
speech corpora to demonstrate, qualitatively, its ability to transform speakers
or linguistic content by manipulating different sets of latent variables; and
quantitatively, its ability to outperform an i-vector baseline for speaker
verification and reduce the word error rate by as much as 35% in mismatched
train/test scenarios for automatic speech recognition tasks.