Disentangling Style Factors from Speaker Representations

Our goal is to separate out speaking style from speaker identity in utterance-level representations of speech such as ivectors and x-vectors. We first show that both i-vectors and x-vectors contain information not only about speaker but also about speaking style (for one data set) or emotion (for another data set), even when projected into a low-dimensional space. To disentangle these factors, we use an autoencoder in which the latent space is split into two subspaces. The entangled information about speaker and style/emotion is pushed apart by the use of auxiliary classifiers that take one of the two latent subspaces as input and that are jointly learned with the autoencoder. We evaluate how well the latent subspaces separate the factors by using them as input to separate style/emotion classification tasks. In traditional speaker identification tasks, speakerinvariant characteristics are factorized from channel and then the channel information is ignored. Our results suggest that this so-called channel may contain exploitable information, which we refer to as style factors. Finally, we propose future work to use information theory to formalize style factors in the context of speaker identity.