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arxiv: 2509.15692 · v2 · submitted 2025-09-19 · 💻 cs.SD · cs.CL· eess.AS

Direct Simultaneous Translation Activation for Large Audio-Language Models

Pei Zhang , Yiming Wang , Jialong Tang , Baosong Yang , Rui Wang , Derek F. Wong , Fei Huang This is my paper

Pith reviewed 2026-05-18 16:25 UTC · model grok-4.3

classification 💻 cs.SD cs.CLeess.AS
keywords simultaneous speech translationlarge audio-language modelsself-augmentationSimulSAdata augmentationreal-time translationspeech-to-text translation
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The pith

Augmenting training data with 1% self-generated simultaneous examples activates real-time speech translation in large audio-language models without any model changes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that large audio-language models already possess the ability to handle simultaneous speech-to-text translation, which can be activated through a targeted data augmentation strategy. By randomly truncating input speech and using the model to generate partially aligned translations, a small set of simultaneous examples is created and mixed into the standard offline training data. This approach closes the gap between training on complete utterances and inference on partial, streaming input. A sympathetic reader cares because it offers a low-cost way to enable real-time translation capabilities in existing powerful models, avoiding the need for complex architectural redesigns or new decoding methods.

Core claim

The central claim is that by utilizing Simultaneous Self-Augmentation (SimulSA) to obtain simultaneous data through random truncation of speech and construction of partially aligned translations with the model's inherent capabilities, and then incorporating this data amounting to about 1% of the full offline SFT data, the Simul-S2TT capabilities of large audio-language models are significantly activated, all without any modifications to the model architecture or decoding strategy.

What carries the argument

Simultaneous Self-Augmentation (SimulSA), a method that generates simultaneous training data by randomly truncating speech inputs and creating partially aligned target translations to align offline training distributions with simultaneous inference needs.

If this is right

  • LALMs gain effective Simul-S2TT performance through data augmentation alone.
  • The need for architectural modifications in simultaneous translation is reduced.
  • Only a small fraction (1%) of additional data is required to achieve significant activation.
  • The distribution gap between offline and simultaneous translation is bridged effectively.
  • Capabilities activate without altering decoding strategies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This data strategy could extend to activating other streaming or real-time behaviors in audio models.
  • The success suggests that many LALMs may have latent simultaneous translation abilities that are underutilized due to training data mismatches.
  • Future work might test if similar truncation methods work for other modalities or tasks.

Load-bearing premise

Randomly truncating speech and constructing partially aligned translations produces a training distribution that is sufficiently similar to the conditions of real simultaneous inference.

What would settle it

Observing no improvement in real-time translation quality or latency when testing the augmented models on streaming speech inputs compared to baseline models trained only on offline data.

read the original abstract

Simultaneous speech-to-text translation (Simul-S2TT) aims to translate speech into target text in real time, outputting translations while receiving source speech input, rather than waiting for the entire utterance to be spoken. Simul-S2TT research often modifies model architectures to implement read-write strategies. However, with the rise of large audio-language models (LALMs), a key challenge is how to directly activate Simul-S2TT capabilities in base models without additional architectural changes. In this paper, we introduce {\bf Simul}taneous {\bf S}elf-{\bf A}ugmentation ({\bf SimulSA}), a strategy that utilizes LALMs' inherent capabilities to obtain simultaneous data by randomly truncating speech and constructing partially aligned translation. By incorporating them into offline SFT data, SimulSA effectively bridges the distribution gap between offline translation during pretraining and simultaneous translation during inference. Experimental results demonstrate that augmenting only about {\bf 1\%} of the simultaneous data, compared to the full offline SFT data, can significantly activate LALMs' Simul-S2TT capabilities without modifications to model architecture or decoding strategy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes Simultaneous Self-Augmentation (SimulSA) to activate simultaneous speech-to-text translation (Simul-S2TT) capabilities in large audio-language models (LALMs). By randomly truncating speech inputs and constructing partially aligned translations, the method augments only about 1% of the offline supervised fine-tuning (SFT) data. The central claim is that this augmentation bridges the distribution gap between offline pretraining and simultaneous inference, enabling Simul-S2TT without any modifications to model architecture or decoding strategy.

Significance. If the result holds, the work would offer a lightweight, architecture-agnostic route to simultaneous translation in LALMs. It would reduce reliance on specialized read/write policies or architectural changes that dominate current Simul-S2TT research, potentially simplifying deployment for real-time applications.

major comments (2)
  1. [Experiments / Results] The experimental section provides no details on the concrete evaluation metrics (e.g., BLEU under fixed latency, Average Lagging, or COMET), the baselines used, the exact procedure for generating partial alignments, or statistical significance testing. Without these, the claim of 'significant activation' from 1% augmentation cannot be verified.
  2. [Method (SimulSA description)] The training procedure in the method section performs offline SFT on complete (truncated) sequences with partial targets. This does not expose the model to incremental read/write decisions that define true simultaneous inference, where the model must choose at each step whether to emit tokens or wait. The reported gains may therefore reflect prefix translation rather than learned timing policy.
minor comments (2)
  1. [Abstract and Section 3] Clarify the exact ratio of augmented simultaneous data to the full offline SFT corpus and report the absolute sizes of both sets.
  2. [Figures] Ensure all figures illustrating truncation and alignment include captions that explicitly contrast the proposed distribution with standard offline training.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment point by point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Experiments / Results] The experimental section provides no details on the concrete evaluation metrics (e.g., BLEU under fixed latency, Average Lagging, or COMET), the baselines used, the exact procedure for generating partial alignments, or statistical significance testing. Without these, the claim of 'significant activation' from 1% augmentation cannot be verified.

    Authors: We agree that the experimental section would benefit from greater detail to support verification and reproducibility. In the revised manuscript we will expand this section to specify all evaluation metrics (including BLEU computed under fixed-latency constraints, Average Lagging, and COMET), list the baselines employed, describe the precise random-truncation and partial-alignment procedure used in SimulSA, and report statistical significance testing (e.g., bootstrap or paired tests). revision: yes

  2. Referee: [Method (SimulSA description)] The training procedure in the method section performs offline SFT on complete (truncated) sequences with partial targets. This does not expose the model to incremental read/write decisions that define true simultaneous inference, where the model must choose at each step whether to emit tokens or wait. The reported gains may therefore reflect prefix translation rather than learned timing policy.

    Authors: We respectfully note that our goal is architecture- and decoding-agnostic activation rather than explicit policy learning. Training on truncated inputs paired with correspondingly partial targets teaches the model to map incomplete audio to usable translations; the autoregressive decoder then produces output incrementally as additional speech arrives. This differs from conventional prefix translation because the targets are aligned to the truncated inputs, directly addressing the offline-to-simultaneous distribution gap. We will add clarifying discussion in the method section to emphasize this distinction and how it enables simultaneous behavior at inference without read/write mechanisms. revision: partial

Circularity Check

0 steps flagged

Empirical augmentation strategy with no closed-form or self-referential reduction

full rationale

The paper presents SimulSA as a practical data-augmentation heuristic (random truncation of speech plus construction of partial targets) that is mixed into offline SFT data at ~1 % ratio. No equations, uniqueness theorems, or fitted parameters are defined in terms of the target Simul-S2TT metric; the claimed activation is reported as an experimental outcome rather than derived by construction from the same inputs. No load-bearing self-citations or ansatzes imported from prior author work are invoked to justify the core premise. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests primarily on the domain assumption that base LALMs already contain latent simultaneous translation abilities that can be surfaced by aligning training and inference distributions; no explicit free parameters or invented entities are stated in the abstract.

axioms (1)
  • domain assumption Large audio-language models possess inherent simultaneous translation capabilities that can be activated through targeted data distribution alignment without architectural modification.
    The paper's approach assumes the base models already encode the necessary behaviors and that the distribution gap is the main obstacle.

pith-pipeline@v0.9.0 · 5751 in / 1212 out tokens · 106086 ms · 2026-05-18T16:25:40.773914+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages · 5 internal anchors

  1. [1]

    Can neural machine translation do simultaneous translation?

    K Cho. Can neural machine translation do simultaneous trans- lation?arXiv Preprint, CoRR, arXiv: abs/1606.02012, 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  2. [2]

    Online and linear-time attention by enforc- ing monotonic alignments

    Colin Raffel, Minh-Thang Luong, Peter J Liu, Ron J Weiss, and Douglas Eck. Online and linear-time attention by enforc- ing monotonic alignments. InInternational conference on ma- chine learning, pages 2837–2846. PMLR, 2017

    work page 2017

  3. [3]

    Monotonic infinite lookback attention for simul- taneous machine translation

    Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, and Colin Raffel. Monotonic infinite lookback attention for simul- taneous machine translation. InProceedings of the 57th An- nual Meeting of the Association for Computational Linguistics, pages 1313–1323, 2019

    work page 2019

  4. [4]

    Sillm: Large language models for simultaneous machine translation.arXiv preprint arXiv:2402.13036, 2024

    Shoutao Guo, Shaolei Zhang, Zhengrui Ma, Min Zhang, and Yang Feng. Sillm: Large language models for simultaneous machine translation.arXiv preprint arXiv:2402.13036, 2024

    work page arXiv 2024

  5. [5]

    Direct segmentation models for streaming speech translation

    Javier Iranzo-S ´anchez, Adri ´an Gim ´enez Pastor, Joan Albert Silvestre Cerd `a, Pau Baquero-Arnal, Jorge Civera Saiz, and Alfons Juan. Direct segmentation models for streaming speech translation. InProceedings of the 2020 Conference on Empiri- cal Methods in Natural Language Processing (EMNLP), pages 2599–2611. Association for Computational Linguistics, 2020

    work page 2020

  6. [6]

    Direct simultaneous speech-to-text translation assisted by syn- chronized streaming asr

    Junkun Chen, Mingbo Ma, Renjie Zheng, and Liang Huang. Direct simultaneous speech-to-text translation assisted by syn- chronized streaming asr. InFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 4618– 4624, 2021

    work page 2021

  7. [7]

    Re- cent advances in end-to-end simultaneous speech translation

    Xiaoqian Liu, Guoqiang Hu, Yangfan Du, Erfeng He, YingFeng Luo, Chen Xu, Tong Xiao, and Jingbo Zhu. Re- cent advances in end-to-end simultaneous speech translation. InProceedings of the Thirty-Third International Joint Confer- ence on Artificial Intelligence, pages 8142–8150, 2024

    work page 2024

  8. [8]

    Learn- ing when to translate for streaming speech

    Qian Dong, Yaoming Zhu, Mingxuan Wang, and Lei Li. Learn- ing when to translate for streaming speech. InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 680–694, 2022

    work page 2022

  9. [9]

    End-to-end simultaneous speech translation with differentiable segmentation

    Shaolei Zhang and Yang Feng. End-to-end simultaneous speech translation with differentiable segmentation. InFind- ings of the Association for Computational Linguistics: ACL 2023, pages 7659–7680, 2023

    work page 2023

  10. [10]

    Decision attentive regularization to improve simultaneous speech translation systems.arXiv preprint arXiv:2110.15729, 2021

    Mohd Abbas Zaidi, Beomseok Lee, Sangha Kim, and Chan- woo Kim. Decision attentive regularization to improve simultaneous speech translation systems.arXiv preprint arXiv:2110.15729, 2021

    work page arXiv 2021

  11. [11]

    Cross attention augmented transducer networks for simultane- ous translation

    Dan Liu, Mengge Du, Xiaoxi Li, Ya Li, and Enhong Chen. Cross attention augmented transducer networks for simultane- ous translation. InProceedings of the 2021 Conference on Em- pirical Methods in Natural Language Processing, pages 39– 55, 2021

    work page 2021

  12. [12]

    Incremental blockwise beam search for simul- taneous speech translation with controllable quality-latency tradeoff.arXiv preprint arXiv:2309.11379, 2023

    Peter Pol ´ak, Brian Yan, Shinji Watanabe, Alex Waibel, and Ondˇrej Bojar. Incremental blockwise beam search for simul- taneous speech translation with controllable quality-latency tradeoff.arXiv preprint arXiv:2309.11379, 2023

    work page arXiv 2023

  13. [13]

    Attention as a guide for simultaneous speech translation

    Sara Papi, Matteo Negri, and Marco Turchi. Attention as a guide for simultaneous speech translation. InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13340–13356, 2023

    work page 2023

  14. [14]

    Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

    Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shil- iang Zhang, Zhijie Yan, Chang Zhou, and Jingren Zhou. Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models.arXiv preprint arXiv:2311.07919, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  15. [15]

    SALMONN: Towards Generic Hearing Abilities for Large Language Models

    Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun Ma, and Chao Zhang. Salmonn: Towards generic hearing abilities for large language models. arXiv preprint arXiv:2310.13289, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  16. [16]

    Speechverse: A large-scale generalizable audio language model.arXiv preprint arXiv:2405.08295, 2024

    Nilaksh Das, Saket Dingliwal, Srikanth Ronanki, Rohit Paturi, Zhaocheng Huang, Prashant Mathur, Jie Yuan, Dhanush Bekal, Xing Niu, Sai Muralidhar Jayanthi, et al. Speechverse: A large-scale generalizable audio language model.arXiv preprint arXiv:2405.08295, 2024

    work page arXiv 2024

  17. [17]

    Qwen2-Audio Technical Report

    Yunfei Chu, Jin Xu, Qian Yang, Haojie Wei, Xipin Wei, Zhi- fang Guo, Yichong Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, et al. Qwen2-audio technical report.arXiv preprint arXiv:2407.10759, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  18. [18]

    Alignsum: Data pyramid hierarchical fine-tuning for aligning with human summarization preference

    Yang Han, Yiming Wang, Rui Wang, Lu Chen, and Kai Yu. Alignsum: Data pyramid hierarchical fine-tuning for aligning with human summarization preference. InFindings of the As- sociation for Computational Linguistics: EMNLP 2024, pages 8506–8522, 2024

    work page 2024

  19. [19]

    A generalization of the beta distribution with applications.Journal of Econometrics, 66(1-2):133–152, 1995

    James B McDonald and Yexiao J Xu. A generalization of the beta distribution with applications.Journal of Econometrics, 66(1-2):133–152, 1995

    work page 1995

  20. [20]

    Fast infer- ence from transformers via speculative decoding

    Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast infer- ence from transformers via speculative decoding. InInterna- tional Conference on Machine Learning, pages 19274–19286. PMLR, 2023

    work page 2023

  21. [21]

    Covost 2 and mas- sively multilingual speech-to-text translation.arXiv preprint arXiv:2007.10310, 2020

    Changhan Wang, Anne Wu, and Juan Pino. Covost 2 and mas- sively multilingual speech-to-text translation.arXiv preprint arXiv:2007.10310, 2020

    work page arXiv 2007

  22. [22]

    LoRA: Low-Rank Adaptation of Large Language Models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models.arXiv preprint arXiv:2106.09685, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  23. [23]

    Bleu: a method for automatic evaluation of machine translation

    Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. InProceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318, 2002

    work page 2002

  24. [24]

    xcomet: Transpar- ent machine translation evaluation through fine-grained error detection.Transactions of the Association for Computational Linguistics, 12:979–995, 2024

    Nuno M Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur, Pierre Colombo, and Andr ´e FT Martins. xcomet: Transpar- ent machine translation evaluation through fine-grained error detection.Transactions of the Association for Computational Linguistics, 12:979–995, 2024

    work page 2024