Transfer Learning from Speech Synthesis to Voice Conversion with Non-Parallel Training Data

TL;DR

This paper introduces a transfer learning framework that leverages a text-to-speech synthesis system to enable non-parallel, any-to-any voice conversion with improved speech quality and naturalness.

Contribution

It presents a novel TTS-VC transfer learning approach that reuses linguistic representations from TTS to train a non-parallel voice conversion system, outperforming existing methods.

Findings

Outperforms phonetic posteriorgram and VAE baselines in quality and naturalness.

Enables non-parallel, any-to-any voice conversion.

Uses shared encoder-decoder architecture for TTS and VC.

Abstract

This paper presents a novel framework to build a voice conversion (VC) system by learning from a text-to-speech (TTS) synthesis system, that is called TTS-VC transfer learning. We first develop a multi-speaker speech synthesis system with sequence-to-sequence encoder-decoder architecture, where the encoder extracts robust linguistic representations of text, and 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, and reuse such a mapping to supervise the training of latent representations of an encoder-decoder voice conversion system. In the voice conversion system, the encoder takes speech instead of text as input, while the decoder is functionally similar to TTS decoder. As we condition the decoder on 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. Experiments show that the proposed approach outperforms two competitive voice conversion baselines consistently, namely phonetic posteriorgram and variational autoencoder methods, in terms of speech quality, naturalness, and speaker similarity.