  Here we present a novel approach to conditioning the SampleRNN generative
model for voice conversion (VC). Conventional methods for VC modify the
perceived speaker identity by converting between source and target acoustic
features. Our approach focuses on preserving voice content and depends on the
generative network to learn voice style. We first train a multi-speaker
SampleRNN model conditioned on linguistic features, pitch contour, and speaker
identity using a multi-speaker speech corpus. Voice-converted speech is
generated using linguistic features and pitch contour extracted from the source
speaker, and the target speaker identity. We demonstrate that our system is
capable of many-to-many voice conversion without requiring parallel data,
enabling broad applications. Subjective evaluation demonstrates that our
approach outperforms conventional VC methods.

Sound;Machine Learning;Audio and Speech Processing