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ConVoice: Real-Time Zero-Shot Voice Style Transfer with Convolutional Network

2020-05-15 22:41:16
Yurii Rebryk, Stanislav Beliaev
arXiv_SD
                 
Abstract

We propose a neural network for zero-shot voice conversion (VC) without any parallel or transcribed data. Our approach uses pre-trained models for automatic speech recognition (ASR) and speaker embedding, obtained from a speaker verification task. Our model is fully convolutional and non-autoregressive except for a small pre-trained recurrent neural network for speaker encoding. ConVoice can convert speech of any length without compromising quality due to its convolutional architecture. Our model has comparable quality to similar state-of-the-art models while being extremely fast.

Abstract (translated)

URL

https://arxiv.org/abs/2005.07815

PDF

https://arxiv.org/pdf/2005.07815.pdf

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