@inproceedings{yuguang-etal-2025-takin,
title = "Takin-{VC}: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling",
author = "Yuguang, Yang and
Pan, Yu and
Yao, Jixun and
Zhang, Xiang and
Ye, Jianhao and
Zhou, Hongbin and
Xie, Lei and
Ma, Lei and
Zhao, Jianjun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.87/",
doi = "10.18653/v1/2025.acl-long.87",
pages = "1731--1742",
ISBN = "979-8-89176-251-0",
abstract = "Expressive zero-shot voice conversion (VC) is a critical and challenging task that aims to transform the source timbre into an arbitrary unseen speaker while preserving the original content and expressive qualities. Despite recent progress in zero-shot VC, there remains considerable potential for improvements in speaker similarity and speech naturalness. Moreover, existing zero-shot VC systems struggle to fully reproduce paralinguistic information in highly expressive speech, such as breathing, crying, and emotional nuances, limiting their practical applicability. To address these issues, we propose Takin-VC, a novel expressive zero-shot VC framework via adaptive hybrid content encoding and memory-augmented context-aware timbre modeling. Specifically, we introduce an innovative hybrid content encoder that incorporates an adaptive fusion module, capable of effectively integrating quantized features of the pre-trained WavLM and HybridFormer in an implicit manner, so as to extract precise linguistic features while enriching paralinguistic elements. For timbre modeling, we propose advanced memory-augmented and context-aware modules to generate high-quality target timbre features and fused representations that seamlessly align source content with target timbre. To enhance real-time performance, we advocate a conditional flow matching model to reconstruct the Mel-spectrogram of the source speech. Experimental results show that our Takin-VC consistently surpasses state-of-the-art VC systems, achieving notable improvements in terms of speech naturalness, speech expressiveness, and speaker similarity, while offering enhanced inference speed."
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<abstract>Expressive zero-shot voice conversion (VC) is a critical and challenging task that aims to transform the source timbre into an arbitrary unseen speaker while preserving the original content and expressive qualities. Despite recent progress in zero-shot VC, there remains considerable potential for improvements in speaker similarity and speech naturalness. Moreover, existing zero-shot VC systems struggle to fully reproduce paralinguistic information in highly expressive speech, such as breathing, crying, and emotional nuances, limiting their practical applicability. To address these issues, we propose Takin-VC, a novel expressive zero-shot VC framework via adaptive hybrid content encoding and memory-augmented context-aware timbre modeling. Specifically, we introduce an innovative hybrid content encoder that incorporates an adaptive fusion module, capable of effectively integrating quantized features of the pre-trained WavLM and HybridFormer in an implicit manner, so as to extract precise linguistic features while enriching paralinguistic elements. For timbre modeling, we propose advanced memory-augmented and context-aware modules to generate high-quality target timbre features and fused representations that seamlessly align source content with target timbre. To enhance real-time performance, we advocate a conditional flow matching model to reconstruct the Mel-spectrogram of the source speech. Experimental results show that our Takin-VC consistently surpasses state-of-the-art VC systems, achieving notable improvements in terms of speech naturalness, speech expressiveness, and speaker similarity, while offering enhanced inference speed.</abstract>
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%0 Conference Proceedings
%T Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling
%A Yuguang, Yang
%A Pan, Yu
%A Yao, Jixun
%A Zhang, Xiang
%A Ye, Jianhao
%A Zhou, Hongbin
%A Xie, Lei
%A Ma, Lei
%A Zhao, Jianjun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yuguang-etal-2025-takin
%X Expressive zero-shot voice conversion (VC) is a critical and challenging task that aims to transform the source timbre into an arbitrary unseen speaker while preserving the original content and expressive qualities. Despite recent progress in zero-shot VC, there remains considerable potential for improvements in speaker similarity and speech naturalness. Moreover, existing zero-shot VC systems struggle to fully reproduce paralinguistic information in highly expressive speech, such as breathing, crying, and emotional nuances, limiting their practical applicability. To address these issues, we propose Takin-VC, a novel expressive zero-shot VC framework via adaptive hybrid content encoding and memory-augmented context-aware timbre modeling. Specifically, we introduce an innovative hybrid content encoder that incorporates an adaptive fusion module, capable of effectively integrating quantized features of the pre-trained WavLM and HybridFormer in an implicit manner, so as to extract precise linguistic features while enriching paralinguistic elements. For timbre modeling, we propose advanced memory-augmented and context-aware modules to generate high-quality target timbre features and fused representations that seamlessly align source content with target timbre. To enhance real-time performance, we advocate a conditional flow matching model to reconstruct the Mel-spectrogram of the source speech. Experimental results show that our Takin-VC consistently surpasses state-of-the-art VC systems, achieving notable improvements in terms of speech naturalness, speech expressiveness, and speaker similarity, while offering enhanced inference speed.
%R 10.18653/v1/2025.acl-long.87
%U https://aclanthology.org/2025.acl-long.87/
%U https://doi.org/10.18653/v1/2025.acl-long.87
%P 1731-1742
Markdown (Informal)
[Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling](https://aclanthology.org/2025.acl-long.87/) (Yuguang et al., ACL 2025)
ACL
- Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, and Jianjun Zhao. 2025. Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1731–1742, Vienna, Austria. Association for Computational Linguistics.