Transfer Learning From Speech Synthesis to Voice Conversion With Non-Parallel Training Data

Abstract

We present a novel voice conversion (VC) framework by learning from a text-to-speech (TTS) synthesis system, that is called TTS-VC transfer learning or TTL-VC for short. We first develop a multi-speaker speech synthesis system with sequence-to-sequence encoder-decoder architecture, where the encoder extracts the linguistic representations of input text, while the decoder, conditioned on target speaker embedding, takes the context vectors and the attention recurrent network cell output to generate target acoustic features. We take advantage of the fact that TTS system maps input text to speaker independent context vectors, thus re-purpose such a mapping to supervise the training of the latent representations of an encoder-decoder voice conversion system. In the voice conversion system, the encoder takes speech instead of text as the input, while the decoder is functionally similar to the TTS decoder. As we condition the decoder on a speaker embedding, the system can be trained on non-parallel data for any-to-any voice conversion. During voice conversion training, we present both text and speech to speech synthesis and voice conversion networks respectively. At run-time, the voice conversion network uses its own encoder-decoder architecture without the need of text input. Experiments show that the proposed TTL-VC system outperforms two competitive voice conversion baselines consistently, namely phonetic posteriorgram and AutoVC methods, in terms of speech quality, naturalness, and speaker similarity.