@inproceedings{wang-etal-2026-m3,
title = "{M}3-{TTS}: Native Two-Stream Multi-Modal Diffusion Transformer for Zero-Shot Speech Synthesis",
author = "Wang, Xiaopeng and
Qiang, Chunyu and
Fu, Ruibo and
Wen, Zhengqi and
Guo, Yuxin and
Liu, Xuefei and
Liu, Yukun and
Liang, Yuzhe and
Yin, Kang and
Ma, Teng and
Chen, Yushen and
Xie, Yuankun and
Li, Chenxing and
Li, Changsheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.240/",
pages = "5061--5071",
ISBN = "979-8-89176-462-0",
abstract = "Zero-shot text-to-speech (TTS) aims to synthesize high-quality speech for unseen speakers using only a short reference utterance. Existing non-autoregressive TTS models typically convert text tokens into speech-length representations through filler-token padding, explicit duration prediction, or text upsampling. However, these approaches either introduce redundant computation or rely on explicit alignment assumptions. We propose M3-TTS, a native two-stream multi-modal diffusion Transformer that preserves the original lengths of text and speech representations and enables direct text{--}speech interaction through packed joint self-attention. M3-TTS employs a dual-stage architecture consisting of Joint-DiT layers for learning text{--}speech correspondence and audio-only DiT layers for acoustic refinement. Reference acoustic information is injected through AdaLN-style conditioning. Experiments on the Seed-TTS benchmark demonstrate strong intelligibility and competitive speaker similarity and perceptual quality. The Mel-VAE variant further improves inference efficiency while maintaining high synthesis quality. Code and demos will be available at https://github.com/WWWWxp/M3-TTS."
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<abstract>Zero-shot text-to-speech (TTS) aims to synthesize high-quality speech for unseen speakers using only a short reference utterance. Existing non-autoregressive TTS models typically convert text tokens into speech-length representations through filler-token padding, explicit duration prediction, or text upsampling. However, these approaches either introduce redundant computation or rely on explicit alignment assumptions. We propose M3-TTS, a native two-stream multi-modal diffusion Transformer that preserves the original lengths of text and speech representations and enables direct text–speech interaction through packed joint self-attention. M3-TTS employs a dual-stage architecture consisting of Joint-DiT layers for learning text–speech correspondence and audio-only DiT layers for acoustic refinement. Reference acoustic information is injected through AdaLN-style conditioning. Experiments on the Seed-TTS benchmark demonstrate strong intelligibility and competitive speaker similarity and perceptual quality. The Mel-VAE variant further improves inference efficiency while maintaining high synthesis quality. Code and demos will be available at https://github.com/WWWWxp/M3-TTS.</abstract>
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%0 Conference Proceedings
%T M3-TTS: Native Two-Stream Multi-Modal Diffusion Transformer for Zero-Shot Speech Synthesis
%A Wang, Xiaopeng
%A Qiang, Chunyu
%A Fu, Ruibo
%A Wen, Zhengqi
%A Guo, Yuxin
%A Liu, Xuefei
%A Liu, Yukun
%A Liang, Yuzhe
%A Yin, Kang
%A Ma, Teng
%A Chen, Yushen
%A Xie, Yuankun
%A Li, Chenxing
%A Li, Changsheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-m3
%X Zero-shot text-to-speech (TTS) aims to synthesize high-quality speech for unseen speakers using only a short reference utterance. Existing non-autoregressive TTS models typically convert text tokens into speech-length representations through filler-token padding, explicit duration prediction, or text upsampling. However, these approaches either introduce redundant computation or rely on explicit alignment assumptions. We propose M3-TTS, a native two-stream multi-modal diffusion Transformer that preserves the original lengths of text and speech representations and enables direct text–speech interaction through packed joint self-attention. M3-TTS employs a dual-stage architecture consisting of Joint-DiT layers for learning text–speech correspondence and audio-only DiT layers for acoustic refinement. Reference acoustic information is injected through AdaLN-style conditioning. Experiments on the Seed-TTS benchmark demonstrate strong intelligibility and competitive speaker similarity and perceptual quality. The Mel-VAE variant further improves inference efficiency while maintaining high synthesis quality. Code and demos will be available at https://github.com/WWWWxp/M3-TTS.
%U https://aclanthology.org/2026.findings-emnlp.240/
%P 5061-5071
Markdown (Informal)
[M3-TTS: Native Two-Stream Multi-Modal Diffusion Transformer for Zero-Shot Speech Synthesis](https://aclanthology.org/2026.findings-emnlp.240/) (Wang et al., Findings 2026)
ACL
- Xiaopeng Wang, Chunyu Qiang, Ruibo Fu, Zhengqi Wen, Yuxin Guo, Xuefei Liu, Yukun Liu, Yuzhe Liang, Kang Yin, Teng Ma, Yushen Chen, Yuankun Xie, Chenxing Li, and Changsheng Li. 2026. M3-TTS: Native Two-Stream Multi-Modal Diffusion Transformer for Zero-Shot Speech Synthesis. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 5061–5071, Budapest, Hungary. Association for Computational Linguistics.