@inproceedings{wang-etal-2026-batonvoice,
title = "{B}aton{V}oice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from {LLM}s",
author = "Wang, Yue and
Ma, Ruotian and
Chen, Xingyu and
Shi, Zhengliang and
Yang, Morunliu and
Chen, Wanshun and
Liu, Huang and
Yao, Jiadi and
He, Xin and
Yang, Qu and
Jiang, Qingxuan and
Ye, Fanghua and
Li, Juntao and
Tu, Zhaopeng and
Li, Xiaolong and
Bo, Liefeng and
Zhang, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2165/",
pages = "46683--46697",
ISBN = "979-8-89176-390-6",
abstract = "The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the linguistic intelligence of these models, typically failing to leverage their powerful instruction-following capabilities. This limitation hinders the model{'}s ability to follow text instructions for controllable Text-to-Speech{~}(TTS). To address this, we propose a new paradigm inspired by operationalism that decouples instruction understanding from speech generation. We introduce BatonVoice, a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan {--} explicit vocal features (e.g., pitch, energy). A separate TTS model, the orchestra, then generates the speech from these features. To realize this component, we develop BatonTTS, a TTS model trained specifically for this task. Our experiments demonstrate that BatonVoice achieves strong performance in controllable and emotional speech synthesis, outperforming strong open- and closed-source baselines. Notably, our approach enables remarkable zero-shot cross-lingual generalization, accurately applying feature control abilities to languages unseen during post-training. This demonstrates that objectifying speech into textual vocal features can more effectively unlock the linguistic intelligence of LLMs."
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<abstract>The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the linguistic intelligence of these models, typically failing to leverage their powerful instruction-following capabilities. This limitation hinders the model’s ability to follow text instructions for controllable Text-to-Speech (TTS). To address this, we propose a new paradigm inspired by operationalism that decouples instruction understanding from speech generation. We introduce BatonVoice, a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan – explicit vocal features (e.g., pitch, energy). A separate TTS model, the orchestra, then generates the speech from these features. To realize this component, we develop BatonTTS, a TTS model trained specifically for this task. Our experiments demonstrate that BatonVoice achieves strong performance in controllable and emotional speech synthesis, outperforming strong open- and closed-source baselines. Notably, our approach enables remarkable zero-shot cross-lingual generalization, accurately applying feature control abilities to languages unseen during post-training. This demonstrates that objectifying speech into textual vocal features can more effectively unlock the linguistic intelligence of LLMs.</abstract>
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%0 Conference Proceedings
%T BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs
%A Wang, Yue
%A Ma, Ruotian
%A Chen, Xingyu
%A Shi, Zhengliang
%A Yang, Morunliu
%A Chen, Wanshun
%A Liu, Huang
%A Yao, Jiadi
%A He, Xin
%A Yang, Qu
%A Jiang, Qingxuan
%A Ye, Fanghua
%A Li, Juntao
%A Tu, Zhaopeng
%A Li, Xiaolong
%A Bo, Liefeng
%A Zhang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-batonvoice
%X The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the linguistic intelligence of these models, typically failing to leverage their powerful instruction-following capabilities. This limitation hinders the model’s ability to follow text instructions for controllable Text-to-Speech (TTS). To address this, we propose a new paradigm inspired by operationalism that decouples instruction understanding from speech generation. We introduce BatonVoice, a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan – explicit vocal features (e.g., pitch, energy). A separate TTS model, the orchestra, then generates the speech from these features. To realize this component, we develop BatonTTS, a TTS model trained specifically for this task. Our experiments demonstrate that BatonVoice achieves strong performance in controllable and emotional speech synthesis, outperforming strong open- and closed-source baselines. Notably, our approach enables remarkable zero-shot cross-lingual generalization, accurately applying feature control abilities to languages unseen during post-training. This demonstrates that objectifying speech into textual vocal features can more effectively unlock the linguistic intelligence of LLMs.
%U https://aclanthology.org/2026.acl-long.2165/
%P 46683-46697
Markdown (Informal)
[BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs](https://aclanthology.org/2026.acl-long.2165/) (Wang et al., ACL 2026)
ACL
- Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, and Min Zhang. 2026. BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 46683–46697, San Diego, California, United States. Association for Computational Linguistics.