Attention-Based WaveNet Autoencoder for Universal Voice Conversion

Abstract

We present a method for converting any voice to a target voice. The method is based on a WaveNet autoencoder, with the addition of a novel attention component that supports the modification of timing between the input and the output samples. Training the attention is done in an unsupervised way, by teaching the neural network to recover the original timing from an artificially modified one. Adding a generic voice robot, which we convert to the target voice, we present a robust Text To Speech pipeline that is able to train without any transcript. Our experiments show that the proposed method is able to recover the timing of the speaker and that the proposed pipeline provides a competitive Text To Speech method.