@inproceedings{feng-etal-2026-mimiclm,
title = "{M}imic{LM}: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora",
author = "Feng, Tao and
Wang, Yuxiang and
Wang, Yuancheng and
Zhang, Xueyao and
Chen, Dekun and
Wang, Chaoren and
Guan, Xun and
Wu, Zhizheng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1020/",
doi = "10.18653/v1/2026.findings-acl.1020",
pages = "20392--20405",
ISBN = "979-8-89176-395-1",
abstract = "Voice imitation aims to transform \textit{source} speech to match a \textit{reference} speaker{'}s timbre and speaking style while preserving linguistic content. A straightforward approach is to train on triplets of \textit{(source, reference, target)}, where \textit{source} and \textit{target} share the same content but \textit{target} matches the \textit{reference}{'}s voice characteristics, yet such data is extremely scarce. Existing approaches either employ carefully designed disentanglement architectures to bypass this data scarcity or leverage external systems to synthesize pseudo-parallel training data. However, the former requires intricate model design, and the latter faces a quality ceiling when synthetic speech is used as training \textit{targets}. To address these limitations, we propose MimicLM, which takes a novel approach by using synthetic speech as training \textit{sources} while retaining real recordings as \textit{targets}. This design enables the model to learn directly from real speech distributions, breaking the synthetic quality ceiling. Building on this data construction approach, we incorporate interleaved text-audio modeling to guide the generation of content-accurate speech and apply post-training with preference alignment to mitigate the inherent distributional mismatch when training on synthetic data. Experiments demonstrate that MimicLM achieves superior voice imitation quality with a simple yet effective architecture, significantly outperforming existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions."
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<abstract>Voice imitation aims to transform source speech to match a reference speaker’s timbre and speaking style while preserving linguistic content. A straightforward approach is to train on triplets of (source, reference, target), where source and target share the same content but target matches the reference’s voice characteristics, yet such data is extremely scarce. Existing approaches either employ carefully designed disentanglement architectures to bypass this data scarcity or leverage external systems to synthesize pseudo-parallel training data. However, the former requires intricate model design, and the latter faces a quality ceiling when synthetic speech is used as training targets. To address these limitations, we propose MimicLM, which takes a novel approach by using synthetic speech as training sources while retaining real recordings as targets. This design enables the model to learn directly from real speech distributions, breaking the synthetic quality ceiling. Building on this data construction approach, we incorporate interleaved text-audio modeling to guide the generation of content-accurate speech and apply post-training with preference alignment to mitigate the inherent distributional mismatch when training on synthetic data. Experiments demonstrate that MimicLM achieves superior voice imitation quality with a simple yet effective architecture, significantly outperforming existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions.</abstract>
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%0 Conference Proceedings
%T MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora
%A Feng, Tao
%A Wang, Yuxiang
%A Wang, Yuancheng
%A Zhang, Xueyao
%A Chen, Dekun
%A Wang, Chaoren
%A Guan, Xun
%A Wu, Zhizheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F feng-etal-2026-mimiclm
%X Voice imitation aims to transform source speech to match a reference speaker’s timbre and speaking style while preserving linguistic content. A straightforward approach is to train on triplets of (source, reference, target), where source and target share the same content but target matches the reference’s voice characteristics, yet such data is extremely scarce. Existing approaches either employ carefully designed disentanglement architectures to bypass this data scarcity or leverage external systems to synthesize pseudo-parallel training data. However, the former requires intricate model design, and the latter faces a quality ceiling when synthetic speech is used as training targets. To address these limitations, we propose MimicLM, which takes a novel approach by using synthetic speech as training sources while retaining real recordings as targets. This design enables the model to learn directly from real speech distributions, breaking the synthetic quality ceiling. Building on this data construction approach, we incorporate interleaved text-audio modeling to guide the generation of content-accurate speech and apply post-training with preference alignment to mitigate the inherent distributional mismatch when training on synthetic data. Experiments demonstrate that MimicLM achieves superior voice imitation quality with a simple yet effective architecture, significantly outperforming existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions.
%R 10.18653/v1/2026.findings-acl.1020
%U https://aclanthology.org/2026.findings-acl.1020/
%U https://doi.org/10.18653/v1/2026.findings-acl.1020
%P 20392-20405
Markdown (Informal)
[MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora](https://aclanthology.org/2026.findings-acl.1020/) (Feng et al., Findings 2026)
ACL
- Tao Feng, Yuxiang Wang, Yuancheng Wang, Xueyao Zhang, Dekun Chen, Chaoren Wang, Xun Guan, and Zhizheng Wu. 2026. MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20392–20405, San Diego, California, United States. Association for Computational Linguistics.