Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Zero-shot Voice Conversion via Self-supervised Prosody Representation
Learning
summary: Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, is an
attractive topic due to its usefulness in real use-case scenarios. Recent work
in this area made progress with disentanglement methods that separate utterance
content and speaker characteristics. Although crucial, extracting disentangled
prosody characteristics for unseen speakers remains an open issue. In this
paper, we propose a novel self-supervised approach to effectively learn the
prosody characteristics. Then, we use the learned prosodic representations to
train our VC model for zero-shot conversion. Our evaluation demonstrates that
we can efficiently extract disentangled prosody representation. Moreover, we
show improved performance compared to the state-of-the-art zero-shot VC models.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning
summary: Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, is an attractive topic due to its usefulness in real use-case scenarios. Recent work in this area made progress with disentanglement methods that separate utterance content and speaker characteristics. Although crucial, extracting disentangled prosody characteristics for unseen speakers remains an open issue. In this paper, we propose a novel self-supervised approach to effectively learn the prosody characteristics. Then, we use the learned prosodic representations to train our VC model for zero-shot conversion. Our evaluation demonstrates that we can efficiently extract disentangled prosody representation. Moreover, we show improved performance compared to the state-of-the-art zero-shot VC models.
id: http://arxiv.org/abs/2110.14422v1
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