  Voice cloning is a highly desired feature for personalized speech interfaces.
Neural network based speech synthesis has been shown to generate high quality
speech for a large number of speakers. In this paper, we introduce a neural
voice cloning system that takes a few audio samples as input. We study two
approaches: speaker adaptation and speaker encoding. Speaker adaptation is
based on fine-tuning a multi-speaker generative model with a few cloning
samples. Speaker encoding is based on training a separate model to directly
infer a new speaker embedding from cloning audios and to be used with a
multi-speaker generative model. In terms of naturalness of the speech and its
similarity to original speaker, both approaches can achieve good performance,
even with very few cloning audios. While speaker adaptation can achieve better
naturalness and similarity, the cloning time or required memory for the speaker
encoding approach is significantly less, making it favorable for low-resource
deployment.
