Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Subband-based Generative Adversarial Network for Non-parallel
Many-to-many Voice Conversion
summary: Voice conversion is to generate a new speech with the source content and a
target voice style. In this paper, we focus on one general setting, i.e.,
non-parallel many-to-many voice conversion, which is close to the real-world
scenario. As the name implies, non-parallel many-to-many voice conversion does
not require the paired source and reference speeches and can be applied to
arbitrary voice transfer. In recent years, Generative Adversarial Networks
(GANs) and other techniques such as Conditional Variational Autoencoders
(CVAEs) have made considerable progress in this field. However, due to the
sophistication of voice conversion, the style similarity of the converted
speech is still unsatisfactory. Inspired by the inherent structure of
mel-spectrogram, we propose a new voice conversion framework, i.e.,
Subband-based Generative Adversarial Network for Voice Conversion (SGAN-VC).
SGAN-VC converts each subband content of the source speech separately by
explicitly utilizing the spatial characteristics between different subbands.
SGAN-VC contains one style encoder, one content encoder, and one decoder. In
particular, the style encoder network is designed to learn style codes for
different subbands of the target speaker. The content encoder network can
capture the content information on the source speech. Finally, the decoder
generates particular subband content. In addition, we propose a pitch-shift
module to fine-tune the pitch of the source speaker, making the converted tone
more accurate and explainable. Extensive experiments demonstrate that the
proposed approach achieves state-of-the-art performance on VCTK Corpus and
AISHELL3 datasets both qualitatively and quantitatively, whether on seen or
unseen data. Furthermore, the content intelligibility of SGAN-VC on unseen data
even exceeds that of StarGANv2-VC with ASR network assistance.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Subband-based Generative Adversarial Network for Non-parallel Many-to-many Voice Conversion
summary: Voice conversion is to generate a new speech with the source content and a target voice style. In this paper, we focus on one general setting, i.e., non-parallel many-to-many voice conversion, which is close to the real-world scenario. As the name implies, non-parallel many-to-many voice conversion does not require the paired source and reference speeches and can be applied to arbitrary voice transfer. In recent years, Generative Adversarial Networks (GANs) and other techniques such as Conditional Variational Autoencoders (CVAEs) have made considerable progress in this field. However, due to the sophistication of voice conversion, the style similarity of the converted speech is still unsatisfactory. Inspired by the inherent structure of mel-spectrogram, we propose a new voice conversion framework, i.e., Subband-based Generative Adversarial Network for Voice Conversion (SGAN-VC). SGAN-VC converts each subband content of the source speech separately by explicitly utilizing the spatial characteristics between different subbands. SGAN-VC contains one style encoder, one content encoder, and one decoder. In particular, the style encoder network is designed to learn style codes for different subbands of the target speaker. The content encoder network can capture the content information on the source speech. Finally, the decoder generates particular subband content. In addition, we propose a pitch-shift module to fine-tune the pitch of the source speaker, making the converted tone more accurate and explainable. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performance on VCTK Corpus and AISHELL3 datasets both qualitatively and quantitatively, whether on seen or unseen data. Furthermore, the content intelligibility of SGAN-VC on unseen data even exceeds that of StarGANv2-VC with ASR network assistance.
id: http://arxiv.org/abs/2207.06057v1
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