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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.
Current voice conversion (VC) methods can successfully convert timbre of the audio. As modeling source audios prosody effectively is a challenging task, there are still limitations of transferring source style to the converted speech. This study prop
The voice conversion challenge is a bi-annual scientific event held to compare and understand different voice conversion (VC) systems built on a common dataset. In 2020, we organized the third edition of the challenge and constructed and distributed
Building cross-lingual voice conversion (VC) systems for multiple speakers and multiple languages has been a challenging task for a long time. This paper describes a parallel non-autoregressive network to achieve bilingual and code-switched voice con
Voice conversion (VC) techniques aim to modify speaker identity of an utterance while preserving the underlying linguistic information. Most VC approaches ignore modeling of the speaking style (e.g. emotion and emphasis), which may contain the factor
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Many style-transfer-inspired methods such as generative adversarial networks (GANs) and variational autoencoders (VAEs) have been proposed. Rece