pith. sign in

arxiv: 2606.25529 · v1 · pith:ADRYX32Jnew · submitted 2026-06-24 · 💻 cs.SD · cs.AI

STEB: A Speech-to-Speech Translation Expressiveness Benchmark for Evaluating Beyond Translation Fidelity

Sitong Cheng , Weizhen Bian , Songjun Cao , Jin Li , Bei Liu , Chunyang Jiang , Yike Zhang , Weihao Wu
show 4 more authors
Yiming Li Chi-Min Chan Long Ma Wei Xue
This is my paper

Pith reviewed 2026-06-25 20:14 UTC · model grok-4.3

classification 💻 cs.SD cs.AI
keywords speech-to-speech translationexpressiveness benchmarkemotion preservationnonverbal vocalizationsS2ST evaluationtranslation fidelityreference-free evaluation
0
0 comments X

The pith

Current speech-to-speech translation systems preserve lexical meaning but struggle to transfer emotion and nonverbal vocalizations.

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

This paper presents STEB, a benchmark for assessing expressiveness in speech-to-speech translation beyond standard fidelity measures. It uses a reference-free method to evaluate how well systems maintain emotion, scenario style, and nonverbal elements across languages. Results from testing six systems show that high translation accuracy does not guarantee expressive alignment, with top scores for emotion at 3.82 out of 5 and nonverbal preservation at 2.31 out of 5. The work positions expressiveness preservation as a distinct and unsolved problem in the field.

Core claim

STEB evaluates S2ST on a 32.6-hour Chinese-English dataset using both conventional metrics and a caption-then-summarize approach with an LLM judge for expressiveness attributes. Across cascaded, end-to-end, and LLM-based systems, strong fidelity performance contrasts with weak results on emotion and NV preservation, demonstrating that semantic transfer and expressive transfer are separate capabilities.

What carries the argument

A caption-then-summarize framework that extracts structured expressive attributes from speech and uses an LLM to compare source and translated attributes without needing aligned references.

If this is right

  • Expressiveness must be treated as a separate objective from meaning preservation in S2ST development.
  • Cascaded systems require specific enhancements to handle emotion and vocalization transfer effectively.
  • Reference-free evaluation becomes essential for expressive dimensions because creating aligned expressive references is impractical at scale.
  • Speech large language models show potential but still fall short on nonverbal preservation compared to fidelity.

Where Pith is reading between the lines

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

  • Applications like real-time interpretation in sensitive contexts may suffer from lost emotional nuance even when words are accurate.
  • Extending the benchmark to additional language pairs could reveal whether the expressiveness gap is language-specific or general.
  • Training methods that incorporate expressiveness feedback directly could be tested using the benchmark's evaluation approach.

Load-bearing premise

The structured attributes extracted by the caption-then-summarize process and judged by the LLM accurately reflect human judgments of expressiveness in speech.

What would settle it

Human listener studies that find no correlation or weak correlation between the benchmark's LLM scores and direct ratings of emotion or NV preservation would invalidate the evaluation method.

Figures

Figures reproduced from arXiv: 2606.25529 by Bei Liu, Chi-Min Chan, Chunyang Jiang, Jin Li, Long Ma, Sitong Cheng, Songjun Cao, Weihao Wu, Wei Xue, Weizhen Bian, Yike Zhang, Yiming Li.

Figure 1
Figure 1. Figure 1: Overview of STEB. (a) Benchmark example showing annotation dimensions: transcription, transla￾tion, rich audio description, emotion, scenario style, and text with NVs. (b) The automatic expressiveness judge: A Captioner–Summary pipeline and NV detection ex￾tract hypothesis annotations, which an LLM Scorer com￾pares against source annotations. (c) Radar chart com￾paring baseline systems across six dimension… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the STEB data curation pipeline. The pipeline consists of six stages: (A) data collection from six real-world scenarios; (B) preprocessing for clean single-speaker utterance extraction; (C) speech quality filtering; (D) transcription, language identification, and translation; (E) expressive annotation for emotion, scenario style, and NVs; and (F) annotation quality checking. The right panels sh… view at source ↗
read the original abstract

Speech-to-speech translation (S2ST) should preserve not only lexical meaning, but also expressive attributes: emotion, scenario style (e.g., news reporting vs. dramatic dialogue), and nonverbal vocalizations (NVs). Moreover, collecting cross-lingual target speech that is both translation-faithful and expressively aligned with the source is difficult at scale, making reference-based evaluation impractical. We introduce STEB (Speech-to-Speech Translation Expressiveness Benchmark), a 32.6-hour Chinese--English benchmark that evaluates both standard dimensions (translation fidelity, speaker similarity, duration alignment) and expressiveness dimensions (emotion, scenario style, NV preservation). For expressiveness evaluation, STEB uses a caption-then-summarize framework that converts speech into structured expressive attributes and compares source and hypothesis attributes with an LLM judge. Human validation shows statistically significant correlations with listener judgments across all expressive dimensions. We evaluate six S2ST systems covering cascaded systems, end-to-end models, and speech large language models. Many systems, especially cascaded ones, achieve strong translation fidelity, but they still struggle with emotion preservation (best: 3.82/5) and NV preservation (best: 2.31/5). These results reveal a gap between semantic transfer and expressive transfer, identifying expressiveness preservation as an open challenge for S2ST. Audio samples are available at https://cmots.github.io/steb.github.io/.

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

1 major / 1 minor

Summary. The manuscript introduces STEB, a 32.6-hour Chinese-English benchmark for speech-to-speech translation (S2ST) that evaluates standard dimensions (translation fidelity, speaker similarity, duration alignment) alongside expressiveness dimensions (emotion, scenario style, and nonverbal vocalization preservation). It employs a caption-then-summarize framework to extract structured attributes from speech and uses an LLM judge for source-hypothesis comparisons on expressiveness, with human validation demonstrating statistically significant correlations to listener judgments. Evaluation of six systems (cascaded, end-to-end, and speech LLMs) shows strong fidelity in many cases but limited expressiveness preservation (best emotion score 3.82/5, best NV score 2.31/5), identifying expressiveness transfer as an open challenge. Audio samples are provided.

Significance. If the LLM-judge pipeline validly tracks human perception of expressiveness, the benchmark is significant for addressing the practical difficulty of collecting expressively aligned cross-lingual references in S2ST. It provides a scalable, reference-free evaluation method with external human validation and an independent LLM judge, while making audio samples available. The results credibly separate semantic from expressive transfer and flag a concrete limitation in current systems.

major comments (1)
  1. [Human validation section] Human validation section: the manuscript states that correlations with listener judgments are statistically significant across expressive dimensions but does not report the correlation coefficients, inter-rater agreement statistics, or per-dimension/per-system robustness. Because the headline claim of an expressiveness gap (and thus the benchmark's validity) rests on the caption-then-summarize + LLM-judge pipeline reliably reflecting human perception, these quantitative details are required to assess whether the observed gap could be partly attributable to metric noise.
minor comments (1)
  1. [Abstract] Abstract: the claim of 'statistically significant correlations' would be strengthened by including the actual coefficient values or at least their range.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment regarding the human validation section. We agree that additional quantitative details will strengthen the presentation of our results and better substantiate the reliability of the LLM-judge pipeline.

read point-by-point responses

  1. Referee: [Human validation section] Human validation section: the manuscript states that correlations with listener judgments are statistically significant across expressive dimensions but does not report the correlation coefficients, inter-rater agreement statistics, or per-dimension/per-system robustness. Because the headline claim of an expressiveness gap (and thus the benchmark's validity) rests on the caption-then-summarize + LLM-judge pipeline reliably reflecting human perception, these quantitative details are required to assess whether the observed gap could be partly attributable to metric noise.

    Authors: We agree with the referee that reporting the specific correlation values and agreement statistics is necessary for a complete assessment. In the revised manuscript we will add: (1) Pearson and Spearman correlation coefficients between the LLM-judge scores and human listener judgments for each expressive dimension (emotion, scenario style, NV preservation); (2) inter-rater agreement statistics (e.g., Krippendorff’s alpha) across the human evaluators; and (3) per-dimension and, where sample sizes permit, per-system breakdowns to demonstrate robustness. These additions will be placed in the human validation subsection and will be accompanied by the corresponding statistical significance values already mentioned in the current text. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines STEB as a new benchmark whose expressiveness scores are produced by an independent caption-then-summarize + LLM-judge pipeline that is then separately validated against human listeners via reported statistically significant correlations. No equations, fitted parameters, or self-citations are shown that would make any reported score or the identified fidelity-expressiveness gap equivalent to its own inputs by construction; the evaluation pipeline and human validation stand as external checks rather than tautological re-statements of the benchmark definition itself.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions in speech processing evaluation plus the domain assumption that structured captions can capture expressive attributes without loss of information relevant to human listeners. No free parameters or invented entities are introduced.

axioms (2)
  • domain assumption LLM-based comparison of structured expressive attributes correlates with human listener judgments of emotion, style, and NV preservation
    Invoked in the caption-then-summarize framework and human validation paragraph of the abstract.
  • standard math Standard speech metrics (translation fidelity, speaker similarity, duration alignment) can be measured independently of expressiveness dimensions
    Implicit in the separation of evaluation dimensions described in the abstract.

pith-pipeline@v0.9.1-grok · 5825 in / 1390 out tokens · 16292 ms · 2026-06-25T20:14:03.455083+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

53 extracted references · 1 canonical work pages

  1. [1]

    Weiss and Fadi Biadsy and Wolfgang Macherey and Melvin Johnson and Zhifeng Chen and Yonghui Wu , title =

    Ye Jia and Ron J. Weiss and Fadi Biadsy and Wolfgang Macherey and Melvin Johnson and Zhifeng Chen and Yonghui Wu , title =. INTERSPEECH , year =

  2. [2]

    ICML , year =

    Ye Jia and Michelle Tadmor Ramanovich and Tal Remez and Roi Pomerantz , title =. ICML , year =

  3. [3]

    Ann Lee and Peng-Jen Chen and Changhan Wang and Jiatao Gu and Sravya Popuri and Xutai Ma and Adam Polyak and Yossi Adi and Qing He and Yun Tang and Juan Pino and Wei-Ning Hsu , title =. Proc. Assoc. Comput. Linguistics (Volume 1: Long Papers) (ACL) , year =

  4. [4]

    2023 , pages =

    Kun Song and Yi Ren and Yi Lei and Chunfeng Wang and Kun Wei and Lei Xie and Xiang Yin and Zejun Ma , title =. 2023 , pages =

    2023

  5. [5]

    Yongqi Wang and Jionghao Bai and Rongjie Huang and Ruiqi Li and Zhiqing Hong and Zhou Zhao , title =. Proc. Assoc. Comput. Linguistics (Volume 4: Student Research Workshop) (ACL) , year =

  6. [6]

    Qianqian Dong and Zhiying Huang and Qiao Tian and Chen Xu and Tom Ko and Yunlong Zhao and Siyuan Feng and Tang Li and Kexin Wang and Xuxin Cheng and Fengpeng Yue and Ye Bai and Xi Chen and Lu Lu and Zejun Ma and Yuping Wang and Mingxuan Wang and Yuxuan Wang , title =

  7. [7]

    Chenyang Le and Yao Qian and Dongmei Wang and Long Zhou and Shujie Liu and Xiaofei Wang and Midia Yousefi and Yanmin Qian and Jinyu Li and Sheng Zhao and Michael Zeng , title =

  8. [8]

    Paul K. Rubenstein and Chulayuth Asawaroengchai and Duc Dung Nguyen and Ankur Bapna and Zalán Borsos and Félix de Chaumont Quitry and Peter Chen and Dalia El Badawy and Wei Han and Eugene Kharitonov and Hannah Muckenhirn and Dirk Padfield and James Qin and Danny Rozenberg and Tara Sainath and Johan Schalkwyk and Matt Sharifi and Michelle Tadmor Ramanovich...

    2023

  9. [9]

    2023 , eprint =

    Tianrui Wang and Long Zhou and Ziqiang Zhang and Yu Wu and Shujie Liu and Yashesh Gaur and Zhuo Chen and Jinyu Li and Furu Wei , title =. 2023 , eprint =

    2023

  10. [10]

    2024 , eprint =

    Hongyu Gong and Bandhav Veluri , title =. 2024 , eprint =

    2024

  11. [11]

    Seamless Communication and Loïc Barrault and Yu-An Chung and Mariano Coria Meglioli and David Dale and Ning Dong and Mark Duppenthaler and Paul-Ambroise Duquenne and Brian Ellis and Hady Elsahar and Justin Haaheim and John Hoffman and Min-Jae Hwang and Hirofumi Inaguma and Christopher Klaiber and Ilia Kulikov and Pengwei Li and Daniel Licht and Jean Maill...

    2023

  12. [12]

    and Markov, K

    Nakamura, S. and Markov, K. and Nakaiwa, H. and Kikui, G. and Kawai, H. and Jitsuhiro, T. and Zhang, J.-S. and Yamamoto, H. and Sumita, E. and Yamamoto, S. , title =. IEEE Trans. Speech Audio Process. , volume =

  13. [13]

    Casacuberta and H

    F. Casacuberta and H. Ney and F.J. Och and E. Vidal and J.M. Vilar and S. Barrachina and I. Garc. Some approaches to statistical and finite-state speech-to-speech translation , journal =

  14. [14]

    2024 , eprint =

    Yifan Peng and Ilia Kulikov and Yilin Yang and Sravya Popuri and Hui Lu and Changhan Wang and Hongyu Gong , title =. 2024 , eprint =

    2024

  15. [15]

    LREC , year =

    Ye Jia and Michelle Tadmor Ramanovich and Quan Wang and Heiga Zen , title =. LREC , year =

  16. [16]

    2022 , pages =

    Alexis Conneau and Min Ma and Simran Khanuja and Yu Zhang and Vera Axelrod and Siddharth Dalmia and Jason Riesa and Clara Rivera and Ankur Bapna , title =. 2022 , pages =

    2022

  17. [17]

    2020 , eprint =

    Changhan Wang and Anne Wu and Juan Pino , title =. 2020 , eprint =

    2020

  18. [18]

    2002 , pages =

    Kishore Papineni and Salim Roukos and Todd Ward and Wei-Jing Zhu , title =. 2002 , pages =

    2002

  19. [19]

    2023 , pages =

    Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever , title =. 2023 , pages =

    2023

  20. [20]

    2022 , pages =

    Zhifu Gao and Shiliang Zhang and Ian McLoughlin and Zhijie Yan , title =. 2022 , pages =

    2022

  21. [21]

    2024 , howpublished =

    Silero Team , title =. 2024 , howpublished =

    2024

  22. [22]

    2025 , eprint=

    Qwen3-Omni Technical Report , author=. 2025 , eprint=

    2025

  23. [23]

    Chi and Quoc V

    Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed H. Chi and Quoc V. Le and Denny Zhou , title =

  24. [24]

    Hirofumi Inaguma and Sravya Popuri and Ilia Kulikov and Peng-Jen Chen and Changhan Wang and Yu-An Chung and Yun Tang and Ann Lee and Shinji Watanabe and Juan Pino , title =. Proc. Assoc. Comput. Linguistics (Volume 1: Long Papers) (ACL) , year =

  25. [25]

    Peloquin and Hongyu Gong and Peng-Jen Chen and Ann Lee , title =

    Min-Jae Hwang and Ilia Kulikov and Benjamin N. Peloquin and Hongyu Gong and Peng-Jen Chen and Ann Lee , title =. 2024 , pages =

    2024

  26. [26]

    2025 , eprint=

    UniSS: Unified Expressive Speech-to-Speech Translation with Your Voice , author=. 2025 , eprint=

    2025

  27. [27]

    2025 , eprint =

    Tom Labiausse and Laurent Mazaré and Edouard Grave and Patrick Pérez and Alexandre Défossez and Neil Zeghidour , title =. 2025 , eprint =

    2025

  28. [28]

    SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations , booktitle =

    Paul-Ambroise Duquenne and Hongyu Gong and Ning Dong and Jingfei Du and Ann Lee and Vedanuj Goswami and Changhan Wang and Juan Pino and Beno. SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations , booktitle =. 2023 , pages =

    2023

  29. [29]

    2025 , eprint=

    Step-Audio 2 Technical Report , author=. 2025 , eprint=

    2025

  30. [30]

    Farinha and Alon Lavie , title =

    Ricardo Rei and Craig Stewart and Ana C. Farinha and Alon Lavie , title =. Proc. Empirical Methods Nat. Lang. Process. (EMNLP) (Volume 1: Long Papers) , year =

  31. [31]

    xcomet: Transparent Machine Translation Evaluation through Fine-grained Error Detection , journal =

    Nuno Miguel Guerreiro and Ricardo Rei and Daan van Stigt and Lu. xcomet: Transparent Machine Translation Evaluation through Fine-grained Error Detection , journal =

  32. [32]

    EMNLP (Findings) , pages =

    Cheng-Han Chiang and Xiaofei Wang and Chung-Ching Lin and Kevin Lin and Linjie Li and Radu Kopetz and Yao Qian and Zhendong Wang and Zhengyuan Yang and Hung-yi Lee and Lijuan Wang , title =. EMNLP (Findings) , pages =

  33. [33]

    2026 , eprint=

    TTS-PRISM: A Perceptual Reasoning and Interpretable Speech Model for Fine-Grained Diagnosis , author=. 2026 , eprint=

    2026

  34. [34]

    2026 , eprint=

    EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models , author=. 2026 , eprint=

    2026

  35. [35]

    ACL , pages =

    Qian Yang and Jin Xu and Wenrui Liu and Yunfei Chu and Ziyue Jiang and Xiaohuan Zhou and Yichong Leng and Yuanjun Lv and Zhou Zhao and Chang Zhou and Jingren Zhou , title =. ACL , pages =

  36. [36]

    Mattia Antonino Di Gangi and Roldano Cattoni and Luisa Bentivogli and Matteo Negri and Marco Turchi , title =

  37. [37]

    Sirou Chen and Sakiko Yahata and Shuichiro Shimizu and Zhengdong Yang and Yihang Li and Chenhui Chu and Sadao Kurohashi , title =

  38. [38]

    EMNLP , pages =

    Maureen de Seyssel and Antony D'Avirro and Adina Williams and Emmanuel Dupoux , title =. EMNLP , pages =

  39. [39]

    ICASSP , pages =

    Herv\'e Bredin and Ruiqing Yin and Juan Manuel Coria and Gregory Gelly and Pavel Korshunov and Marvin Lavechin and Diego Fustes and Hadrien Titeux and Wassim Bouaziz and Marie-Philippe Gill , title =. ICASSP , pages =

  40. [40]

    INTERSPEECH , pages =

    Hui Wang and Siqi Zheng and Yafeng Chen and Luyao Cheng and Qian Chen , title =. INTERSPEECH , pages =

  41. [41]

    Chandan K. A. Reddy and Vishak Gopal and Ross Cutler , title =. ICASSP , pages =

  42. [42]

    2026 , eprint=

    Qwen3-ASR Technical Report , author=. 2026 , eprint=

    2026

  43. [43]

    Effective Pre-Training of Audio Transformers for Sound Event Detection , booktitle =

    Florian Schmid and Tobias Morocutti and Francesco Foscarin and Jan Sch. Effective Pre-Training of Audio Transformers for Sound Event Detection , booktitle =

  44. [44]

    ICASSP , pages =

    Wei Tsung Lu and Ju-Chiang Wang and Qiuqiang Kong and Yun-Ning Hung , title =. ICASSP , pages =

  45. [45]

    2025 , eprint=

    Qwen3 Technical Report , author=. 2025 , eprint=

    2025

  46. [46]

    Chen , title =

    Bin Wang and Xunlong Zou and Geyu Lin and Shuo Sun and Zhuohan Liu and Wenyu Zhang and Zhengyuan Liu and AiTi Aw and Nancy F. Chen , title =. Proc. North Am. Chapter Assoc. Comput. Linguistics (NAACL) (Long Papers) , year =

  47. [47]

    2025 , eprint=

    Seed LiveInterpret 2.0: End-to-end Simultaneous Speech-to-speech Translation with Your Voice , author=. 2025 , eprint=

    2025

  48. [48]

    Sanyuan Chen and Yu Wu and Chengyi Wang and Shujie Liu and Daniel Tompkins and Zhuo Chen and Wanxiang Che and Xiangzhan Yu and Furu Wei , title =. Proc. Int. Conf. Mach. Learn. (ICML) , year =

  49. [49]

    2023 , pages =

    Yihan Wu and Junliang Guo and Xu Tan and Chen Zhang and Bohan Li and Ruihua Song and Lei He and Sheng Zhao and Arul Menezes and Jiang Bian , title =. 2023 , pages =

    2023

  50. [50]

    2025 , url =

    A New Era of Intelligence with Gemini 3 , author =. 2025 , url =

    2025

  51. [51]

    GitHub , year =

    VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning , author =. GitHub , year =

  52. [52]

    UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data , booktitle =

    Chengyi Wang and Yu Wu and Yao Qian and Kenichi Kumatani and Shujie Liu and Furu Wei and Michael Zeng and Xuedong Huang , editor =. UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data , booktitle =. 2021 , url =

    2021

  53. [53]

    The American Journal of Psychology 15, 72–101

    The Proof and Measurement of Association Between Two Things , author=. The American Journal of Psychology , volume=. 1904 , publisher=. doi:10.2307/1412159 , url=

    work page doi:10.2307/1412159 1904