@inproceedings{qiang-etal-2026-unisonate,
title = "{U}ni{S}onate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions",
author = "Qiang, Chunyu and
Wang, Xiaopeng and
Yin, Kang and
Liang, Yuzhe and
Guo, Yuxin and
Ma, Teng and
Zhang, Ziyu and
Wang, Tianrui and
Gong, Cheng and
Chen, Yushen and
Fu, Ruibo and
Wang, Longbiao and
Dang, Jianwu",
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.1293/",
pages = "28043--28054",
ISBN = "979-8-89176-390-6",
abstract = "Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each operating under heterogeneous control paradigms. Unifying these modalities remains a fundamental challenge due to the intrinsic dissonance between structured semantic representations (speech/music) and unstructured acoustic textures (sound effects). In this paper, we introduce \textbf{UniSonate}, a unified flow-matching framework capable of synthesizing speech, music, and sound effects through a standardized, reference-free natural language instruction interface. To reconcile structural disparities, we propose a novel dynamic token injection mechanism that projects unstructured environmental sounds into a structured temporal latent space, enabling precise duration control within a phoneme-driven Multimodal Diffusion Transformer (MM-DiT). Coupled with a multi-stage curriculum learning strategy, this approach effectively mitigates cross-modal optimization conflicts. Extensive experiments demonstrate that UniSonate achieves state-of-the-art performance in instruction-based TTS (WER 1.47{\%}) and TTM (SongEval Coherence 3.18), while maintaining competitive fidelity in TTA. Crucially, we observe \textit{positive transfer}, where joint training on diverse audio data significantly enhances structural coherence and prosodic expressiveness compared to single-task baselines."
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<abstract>Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each operating under heterogeneous control paradigms. Unifying these modalities remains a fundamental challenge due to the intrinsic dissonance between structured semantic representations (speech/music) and unstructured acoustic textures (sound effects). In this paper, we introduce UniSonate, a unified flow-matching framework capable of synthesizing speech, music, and sound effects through a standardized, reference-free natural language instruction interface. To reconcile structural disparities, we propose a novel dynamic token injection mechanism that projects unstructured environmental sounds into a structured temporal latent space, enabling precise duration control within a phoneme-driven Multimodal Diffusion Transformer (MM-DiT). Coupled with a multi-stage curriculum learning strategy, this approach effectively mitigates cross-modal optimization conflicts. Extensive experiments demonstrate that UniSonate achieves state-of-the-art performance in instruction-based TTS (WER 1.47%) and TTM (SongEval Coherence 3.18), while maintaining competitive fidelity in TTA. Crucially, we observe positive transfer, where joint training on diverse audio data significantly enhances structural coherence and prosodic expressiveness compared to single-task baselines.</abstract>
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%0 Conference Proceedings
%T UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions
%A Qiang, Chunyu
%A Wang, Xiaopeng
%A Yin, Kang
%A Liang, Yuzhe
%A Guo, Yuxin
%A Ma, Teng
%A Zhang, Ziyu
%A Wang, Tianrui
%A Gong, Cheng
%A Chen, Yushen
%A Fu, Ruibo
%A Wang, Longbiao
%A Dang, Jianwu
%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 qiang-etal-2026-unisonate
%X Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each operating under heterogeneous control paradigms. Unifying these modalities remains a fundamental challenge due to the intrinsic dissonance between structured semantic representations (speech/music) and unstructured acoustic textures (sound effects). In this paper, we introduce UniSonate, a unified flow-matching framework capable of synthesizing speech, music, and sound effects through a standardized, reference-free natural language instruction interface. To reconcile structural disparities, we propose a novel dynamic token injection mechanism that projects unstructured environmental sounds into a structured temporal latent space, enabling precise duration control within a phoneme-driven Multimodal Diffusion Transformer (MM-DiT). Coupled with a multi-stage curriculum learning strategy, this approach effectively mitigates cross-modal optimization conflicts. Extensive experiments demonstrate that UniSonate achieves state-of-the-art performance in instruction-based TTS (WER 1.47%) and TTM (SongEval Coherence 3.18), while maintaining competitive fidelity in TTA. Crucially, we observe positive transfer, where joint training on diverse audio data significantly enhances structural coherence and prosodic expressiveness compared to single-task baselines.
%U https://aclanthology.org/2026.acl-long.1293/
%P 28043-28054
Markdown (Informal)
[UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions](https://aclanthology.org/2026.acl-long.1293/) (Qiang et al., ACL 2026)
ACL
- Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, and Jianwu Dang. 2026. UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 28043–28054, San Diego, California, United States. Association for Computational Linguistics.