Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive Learning with Negative Samples Augmentation
summary: Better disentanglement of speech representation is essential to improve the
quality of voice conversion. Recently contrastive learning is applied to voice
conversion successfully based on speaker labels. However, the performance of
model will reduce in conversion between similar speakers. Hence, we propose an
augmented negative sample selection to address the issue. Specifically, we
create hard negative samples based on the proposed speaker fusion module to
improve learning ability of speaker encoder. Furthermore, considering the
fine-grain modeling of speaker style, we employ a reference encoder to extract
fine-grained style and conduct the augmented contrastive learning on global
style. The experimental results show that the proposed method outperforms
previous work in voice conversion tasks.
Thunk you very much for contribution!
Your judgement is refrected in arXivSearches.json, and is going to be used for VCLab's activity.
Thunk you so much.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive Learning with Negative Samples Augmentation
summary: Better disentanglement of speech representation is essential to improve the quality of voice conversion. Recently contrastive learning is applied to voice conversion successfully based on speaker labels. However, the performance of model will reduce in conversion between similar speakers. Hence, we propose an augmented negative sample selection to address the issue. Specifically, we create hard negative samples based on the proposed speaker fusion module to improve learning ability of speaker encoder. Furthermore, considering the fine-grain modeling of speaker style, we employ a reference encoder to extract fine-grained style and conduct the augmented contrastive learning on global style. The experimental results show that the proposed method outperforms previous work in voice conversion tasks.
id: http://arxiv.org/abs/2311.08670v1
judge
Write [vclab::confirmed] or [vclab::excluded] in comment.