Many-to-Many Voice Conversion based on Bottleneck Features with Variational Autoencoder for Non-parallel Training Data

This paper proposes a novel approach to many-to-many (M2M) voice conversion for non-parallel training data. In the proposed approach, we first obtain bottleneck features (BNFs) as speaker representations from a deep neural network (DNN). Then, a variational autoencoder (VAE) implements the mapping function (i.e., a reconstruction process) using both the latent semantic information and the speaker representations. Furthermore, we propose an adaptive scheme by intervening the training process of the DNN, which can enrich the target speaker's personality feature space in the case of limited training data. Our approach has three advantages: 1) neither parallel training data nor explicit frame alignment process is required; 2) consolidates multiple pair-wise systems into a single M2M model (many-source speakers to many-target speakers); 3) expands M2M conversion task from closed set to open set when the training data of target speaker is very limited. The objective and subjective evaluations show that our proposed approach outperforms the baseline system.