Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: DRVC: A Framework of Any-to-Any Voice Conversion with Self-Supervised
Learning
summary: Any-to-any voice conversion problem aims to convert voices for source and
target speakers, which are out of the training data. Previous works wildly
utilize the disentangle-based models. The disentangle-based model assumes the
speech consists of content and speaker style information and aims to untangle
them to change the style information for conversion. Previous works focus on
reducing the dimension of speech to get the content information. But the size
is hard to determine to lead to the untangle overlapping problem. We propose
the Disentangled Representation Voice Conversion (DRVC) model to address the
issue. DRVC model is an end-to-end self-supervised model consisting of the
content encoder, timbre encoder, and generator. Instead of the previous work
for reducing speech size to get content, we propose a cycle for restricting the
disentanglement by the Cycle Reconstruct Loss and Same Loss. The experiments
show there is an improvement for converted speech on quality and voice
similarity.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: DRVC: A Framework of Any-to-Any Voice Conversion with Self-Supervised Learning
summary: Any-to-any voice conversion problem aims to convert voices for source and target speakers, which are out of the training data. Previous works wildly utilize the disentangle-based models. The disentangle-based model assumes the speech consists of content and speaker style information and aims to untangle them to change the style information for conversion. Previous works focus on reducing the dimension of speech to get the content information. But the size is hard to determine to lead to the untangle overlapping problem. We propose the Disentangled Representation Voice Conversion (DRVC) model to address the issue. DRVC model is an end-to-end self-supervised model consisting of the content encoder, timbre encoder, and generator. Instead of the previous work for reducing speech size to get content, we propose a cycle for restricting the disentanglement by the Cycle Reconstruct Loss and Same Loss. The experiments show there is an improvement for converted speech on quality and voice similarity.
id: http://arxiv.org/abs/2202.10976v1
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