Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion
summary: Voice conversion (VC) can be achieved by first extracting source content
information and target speaker information, and then reconstructing waveform
with these information. However, current approaches normally either extract
dirty content information with speaker information leaked in, or demand a large
amount of annotated data for training. Besides, the quality of reconstructed
waveform can be degraded by the mismatch between conversion model and vocoder.
In this paper, we adopt the end-to-end framework of VITS for high-quality
waveform reconstruction, and propose strategies for clean content information
extraction without text annotation. We disentangle content information by
imposing an information bottleneck to WavLM features, and propose the
spectrogram-resize based data augmentation to improve the purity of extracted
content information. Experimental results show that the proposed method
outperforms the latest VC models trained with annotated data and has greater
robustness.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion
summary: Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information. However, current approaches normally either extract dirty content information with speaker information leaked in, or demand a large amount of annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for high-quality waveform reconstruction, and propose strategies for clean content information extraction without text annotation. We disentangle content information by imposing an information bottleneck to WavLM features, and propose the spectrogram-resize based data augmentation to improve the purity of extracted content information. Experimental results show that the proposed method outperforms the latest VC models trained with annotated data and has greater robustness.
id: http://arxiv.org/abs/2210.15418v1
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