Hierarchical Generative Modeling for Controllable Speech Synthesis
This paper proposes a neural end-to-end text-to-speech model which can control latent attributes in the generation of speech, that are rarely annotated in the training data (e.g. speaking styles, accents, background noise level, and recording conditions). The model is formulated as a conditional generative model with two levels of hierarchical latent variables. The first level is a categorical variable, which represents attribute groups (e.g. clean/noisy) and provides interpretability. The second level, conditioned on the first, is a multivariate Gaussian variable, which characterizes specific attribute configurations (e.g. noise level, speaking rate) and enables disentangled fine-grained control over these attributes. This amounts to using a Gaussian mixture model (GMM) for the latent distribution. Extensive evaluation of the proposed model demonstrates its ability to control the aforementioned attributes. In particular, it is capable of consistently synthesizing high-quality clean speech regardless of the quality of the training data for the target speaker.