Neural Voice Cloning: Teaching Machines to Generate Speech

At Baidu Research, we aim to revolutionize human-machine interfaces with the latest artificial intelligence techniques. Our Deep Voice project was started a year ago , which focuses on teaching machines to generate speech from text that sounds more human-like.

Beyond single-speaker speech synthesis, we demonstrated that a single system could learn to reproduce thousands of speaker identities, with less than half an hour of training data for each speaker. This capability was enabled by learning shared and discriminative information from speakers.

We were motivated to push this idea even further, and attempted to learn speaker characteristics from only a few utterances (i.e., sentences of few seconds duration). This problem is commonly known as “voice cloning.” Voice cloning is expected to have significant applications in the direction of personalization in human-machine interfaces.


In this study, we focus on two fundamental approaches for solving the problems with voice cloning: speaker adaptation and speaker encoding (please see the above figure above for more details). Both techniques can be adapted to a multi-speaker generative speech model with speaker embeddings, without degrading its quality. In terms of naturalness of the speech and similarity to the original speaker, both demonstrate good performance, even with very few cloning audios. Please find the cloned audio samples here.

Speaker adaptation is based on fine-tuning a multi-speaker generative model with a few cloning samples, by using backpropagation-based optimization. Adaptation can be applied to the whole model, or only the low-dimensional speaker embeddings. The latter enables a much lower number of parameters to represent each speaker, albeit it yields a longer cloning time and lower audio quality.