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arxiv: 2604.15929 · v2 · pith:SKE2NBLRnew · submitted 2026-04-17 · 💻 cs.CL

MUSCAT: MUltilingual, SCientific ConversATion Benchmark

Supriti Sinhamahapatra , Thai-Binh Nguyen , Yi\u{g}it O\u{g}uz , Enes Ugan , Jan Niehues , Alexander Waibel This is my paper

Pith reviewed 2026-05-21 00:39 UTC · model grok-4.3

classification 💻 cs.CL
keywords multilingual speech recognitioncode-switchingASR benchmarkscientific conversationsbilingual discussionsautomatic speech recognitionmultilingual ASRaudio segmentation
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The pith

A new benchmark of bilingual scientific discussions shows that current ASR systems still struggle with mixed languages and code-switching.

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

The paper introduces a benchmark of conversations where multiple speakers discuss scientific papers, each using a different language to produce realistic mixed input. This setup directly tests automatic speech recognition on challenges like code-switching, domain-specific scientific terms, and multilingual flow that everyday systems encounter. The authors supply an evaluation approach that moves past basic word error rate to support consistent cross-language comparisons. Results indicate that even advanced systems find the data difficult, leaving a gap in achieving seamless multilingual speech technology.

Core claim

The authors construct a dataset of bilingual discussions on scientific papers in which each participant speaks in a distinct language, generating natural instances of code-switching and technical vocabulary. Experiments with this data establish that state-of-the-art ASR systems continue to face substantial difficulties in these conditions.

What carries the argument

The MUSCAT benchmark dataset of bilingual scientific paper discussions, which produces mixed-language audio with code-switching and specialized terms to measure ASR robustness.

If this is right

  • ASR development must prioritize better handling of code-switched scientific speech.
  • Evaluation of multilingual systems should incorporate metrics beyond word error rate for fair comparisons.
  • The benchmark supplies a fixed test for tracking progress in managing domain-specific vocabulary during conversations.
  • Consistent cross-language performance measurement becomes possible with the provided framework.

Where Pith is reading between the lines

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

  • Models improved on this data could support more reliable transcription during international research meetings.
  • The approach of using paper discussions could be adapted to create tests for other technical or professional domains.
  • Better performance here might accelerate integration of ASR with real-time translation tools for global collaboration.

Load-bearing premise

The created bilingual discussions on scientific papers sufficiently capture real-world challenges of mixed multilingual input, specific vocabulary, and code-switching.

What would settle it

Showing that leading ASR systems reach word error rates on this dataset similar to their performance on standard single-language benchmarks would falsify the claim that it remains an open challenge.

Figures

Figures reproduced from arXiv: 2604.15929 by Alexander Waibel, Enes Ugan, Jan Niehues, Supriti Sinhamahapatra, Thai-Binh Nguyen, Yi\u{g}it O\u{g}uz.

Figure 1
Figure 1. Figure 1: An example illustrating the creation of MUSCAT (upper part of the figure) and the chal￾lenges its multilingual diversity poses for state-of￾the-art ASR systems (lower part of the figure). The ASR is unable to accurately detect the language switches in a spontaneous conversation denoted by red in the transcript. The blue dashed lines (− − −) represent the part of the conversation that ASR fails to transcrib… view at source ↗
read the original abstract

The goal of multilingual speech technology is to facilitate seamless communication between individuals speaking different languages, creating the experience as though everyone were a multilingual speaker. To create this experience, speech technology needs to address several challenges: Handling mixed multilingual input, specific vocabulary, and code-switching. However, there is currently no dataset benchmarking this situation. We propose a new benchmark to evaluate current Automatic Speech Recognition (ASR) systems, whether they are able to handle these challenges. The benchmark consists of bilingual discussions on scientific papers between multiple speakers, each conversing in a different language. We provide a standard evaluation framework, beyond Word Error Rate (WER) enabling consistent comparison of ASR performance across languages. Experimental results demonstrate that the proposed dataset is still an open challenge for state-of-the-art ASR systems. The dataset is available in https://huggingface.co/datasets/goodpiku/muscat-eval. Keywords: multilingual, speech recognition, audio segmentation, speaker diarization

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 introduces MUSCAT, a benchmark consisting of bilingual discussions on scientific papers in which each speaker uses a different language. The dataset is designed to evaluate ASR systems on mixed multilingual input, domain-specific vocabulary, and code-switching. The authors supply a standard evaluation framework extending beyond Word Error Rate and report experimental results indicating that state-of-the-art ASR systems continue to struggle with the data. The dataset is released publicly on Hugging Face.

Significance. If the constructed conversations contain realistic rates of code-switching and paper-specific technical terminology, the benchmark would address a clear gap in multilingual ASR evaluation. The public release and extended evaluation framework are positive features that support reproducibility and cross-system comparison.

major comments (2)
  1. [§3] §3 (Dataset Construction): The description of how the bilingual scientific discussions were created does not report quantitative measures of intra-utterance code-switching frequency or the proportion of paper-specific technical terms versus general vocabulary. Without these statistics it is difficult to verify that observed ASR errors test the claimed real-world challenges rather than properties of the scripted construction.
  2. [§5] §5 (Experiments): The claim that the dataset remains an open challenge for SOTA ASR systems is presented without baseline comparisons on monolingual scientific speech or on existing code-switched corpora. Such controls are needed to isolate the contribution of multilingual mixing and domain vocabulary to the reported error rates.
minor comments (2)
  1. [Abstract] Keywords list includes 'audio segmentation, speaker diarization' but the main text should explicitly state whether these tasks are part of the proposed evaluation framework or treated as separate components.
  2. [Figures/Tables] Figure captions and table headers should be expanded to make the evaluation metrics and language pairs immediately clear without reference to the main text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below and indicate the revisions planned for the next version.

read point-by-point responses

  1. Referee: [§3] §3 (Dataset Construction): The description of how the bilingual scientific discussions were created does not report quantitative measures of intra-utterance code-switching frequency or the proportion of paper-specific technical terms versus general vocabulary. Without these statistics it is difficult to verify that observed ASR errors test the claimed real-world challenges rather than properties of the scripted construction.

    Authors: We thank the referee for this suggestion. The dataset is designed with each speaker using a single language throughout their turns, producing inter-speaker and inter-utterance switches rather than intra-utterance code-switching. We will revise §3 to add quantitative statistics, including the average number of language switches per conversation and the proportion of paper-specific technical terms (identified via term extraction from the source papers) versus general vocabulary. These measures will clarify that the observed errors align with the targeted multilingual and domain challenges. revision: yes

  2. Referee: [§5] §5 (Experiments): The claim that the dataset remains an open challenge for SOTA ASR systems is presented without baseline comparisons on monolingual scientific speech or on existing code-switched corpora. Such controls are needed to isolate the contribution of multilingual mixing and domain vocabulary to the reported error rates.

    Authors: We agree that baselines would aid interpretation. However, existing code-switched corpora lack the scientific domain focus and the specific bilingual discussion format of MUSCAT. We will revise §5 to include a discussion of these differences and, where feasible, add results on monolingual scientific speech subsets derived from the same papers to help isolate the effects of mixing and specialized vocabulary. revision: partial

Circularity Check

0 steps flagged

No circularity: new dataset and external ASR evaluation are self-contained

full rationale

The paper constructs a new bilingual scientific discussion dataset and reports ASR error rates on it using standard WER plus an extended multilingual evaluation framework. No equations, fitted parameters, or derivations appear; the central claim that the data remains challenging for SOTA systems rests on direct testing of external models rather than any reduction to prior self-referential quantities or self-citations. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Benchmark paper with no free parameters, mathematical axioms, or invented entities; relies on standard ASR evaluation practices and the new dataset itself.

pith-pipeline@v0.9.0 · 5721 in / 838 out tokens · 24649 ms · 2026-05-21T00:39:56.033780+00:00 · methodology

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Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 4 internal anchors

  1. [1]

    Theultimate goal is to have a natural, multilingual conversation where each participant talks in their favorite lan- guage and is able to understand all the other lan- guages

    Introduction Seamless communication across language bound- ariesisalong-termdreamofmankind. Theultimate goal is to have a natural, multilingual conversation where each participant talks in their favorite lan- guage and is able to understand all the other lan- guages. While significant progress has been made in terms of multilingual speech recognition in h...

    work page 2023

  2. [2]

    MUSCAT: MUltilingual, SCientific ConversATion Benchmark

    Data Collection We aim to build a high-quality multilingual dataset. In order to achieve this, we first create a conver- sation setup where the challenges of multilingual, scientific conversations are highlighted. Next, we 1https://huggingface.co/datasets/ goodpiku/muscat-eval arXiv:2604.15929v1 [cs.CL] 17 Apr 2026 13 - - - - - - - - - ASR transcript Engl...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  3. [3]

    In a first step, we perform a manual segmentation of the audio recordings which serves as the oracle to evaluate and compare two automatic segmenta- tion approaches

    Human Annotation We annotate the collected data to be used as a benchmark for state-of-the-art ASR systems. In a first step, we perform a manual segmentation of the audio recordings which serves as the oracle to evaluate and compare two automatic segmenta- tion approaches. Next, we create the multilingual transcripts of the audio. 3.1. Manual Segmentation...

    work page 2023

  4. [4]

    Each recording is between a pair of speakers, and there exists one speaker who is present in two recordings

    MUSCAT Dataset The MUSCAT dataset consists of multilingual con- versations of six recordings across eleven speak- ers. Each recording is between a pair of speakers, and there exists one speaker who is present in two recordings. All six recordings have at least one English speaker, while the other speaks one of the languages from German, Turkish, Chinese, ...

    work page

  5. [5]

    Baseline This section outlines the baseline configuration adoptedinourexperiments,detailingtheASRmod- els used and the segmentation strategies applied during pre-processing. 5.1. ASR Models Our goal is to evaluate the performance of SOTA ASR models on the MUSCAT dataset. To this end, we employ four SOTA models, Whis- per, SALMONN,Phi-4 Multimodal and Wav2...

    work page 2023

  6. [6]

    Through this analysis under varying segmentation and tran- scription conditions, we identify key challenges that the dataset presents for current ASR technology

    Evaluation We evaluate SOTA ASR systems to establish a baseline performance on this dataset. Through this analysis under varying segmentation and tran- scription conditions, we identify key challenges that the dataset presents for current ASR technology. MetricsWord Error Rate (WER) is a common metric used to evaluate the accuracy of ASR sys- tems. It mea...

    work page 2025

  7. [7]

    Existing general-purpose conver- sationaldatasetssuchasMultiWOZ(Budzianowski etal.,2018),DialoGPT(Zhangetal.,2019),(Lietal.,

    Related Work Our work presents a novel dataset that bridges the gap between conversational, multilingual, and aca- demic domains. Existing general-purpose conver- sationaldatasetssuchasMultiWOZ(Budzianowski etal.,2018),DialoGPT(Zhangetal.,2019),(Lietal.,

    work page 2018

  8. [8]

    and ConvAI2(Dinan et al., 2020), primarily focus on casual dialogue, including structured in- teractions or discussions extracted from platforms like Reddit. Other speech datasets include the AMI Meeting Corpus(Kraaij et al., 2005) which consists of meeting recordings and DIPCO(Van Segbroeck et al., 2019), a dataset with natural conversation around a dinn...

    work page 2020

  9. [9]

    introduces a large-scale, 1600-hour bench- mark that addresses the complexities of turn-taking and code-switching across 11 languages. These datasets complement established benchmarks like ML-SUPERB 2.0 (Shi et al., 2024), which expands evaluation to 142 languages to test the cross- lingual generalization of foundational speech mod- els. In addition to th...

    work page 2024

  10. [10]

    Our dataset encompasses scientific conversations in five lan- guages, including English, German, Chinese, Turk- ish, and Vietnamese

    Conclusion This paper proposes a novel multilingual dataset to evaluate current ASR systems. Our dataset encompasses scientific conversations in five lan- guages, including English, German, Chinese, Turk- ish, and Vietnamese. Each conversation consists of a paired speech in two languages, one of which is always English, while the other is one of the four ...

    work page

  11. [11]

    First, the overall scale of the corpus is relatively small, comprising approximately 65 minutes of au- dioand9,066words

    Limitation WhiletheMUSCATdatasetprovidesanovelbench- mark for evaluating multilingual scientific conver- sations, several limitations must be acknowledged. First, the overall scale of the corpus is relatively small, comprising approximately 65 minutes of au- dioand9,066words. Second, althoughthedataset encompasses five distinct languages, there is an imba...

    work page

  12. [12]

    101213369, project DVPS (Diversibus Viis Plurima Solvo)

    Acknowledgement This work was supported by the European Union’s Horizon Europe Framework Programme under grant agreement No. 101213369, project DVPS (Diversibus Viis Plurima Solvo). Additional support was provided by KiKIT (Pi- lot Program for Core-Informatics at KIT) of the Helmholtz Association. We also acknowledge the use of the HoreKa supercomputer, f...

    work page

  13. [13]

    Bibliographical References Abdelrahman Abouelenin, Atabak Ashfaq, Adam Atkinson, Hany Awadalla, Nguyen Bach, Jian- min Bao, Alon Benhaim, Martin Cai, Vishrav Chaudhary, Congcong Chen, et al. 2025. Phi- 4-mini technical report: Compact yet powerful multimodallanguagemodelsviamixture-of-loras. arXiv preprint arXiv:2503.01743. Joshua Ainslie, James Lee-Thorp...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  14. [14]

    Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al

    Beats: Audio pre-training with acoustic tokenizers.arXiv preprint arXiv:2212.09058. Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt qual- ity.See https://vicuna. lmsys. org (accessed 14 Apr...

    work page arXiv 2023

  15. [15]

    Textbooks Are All You Need

    Textbooks are all you need.arXiv preprint arXiv:2306.11644. Injy Hamed, Pavel Denisov, Chia-Yu Li, Mohamed Elmahdy, Slim Abdennadher, and Ngoc Thang Vu. 2022. Investigations on speech recogni- tion systems for low-resource dialectal arabic– english code-switching speech.Computer Speech & Language, 72:101278. Injy Hamed, Ngoc Thang Vu, and Slim Abdennad- h...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  16. [16]

    DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

    The cstr system for multilingual and code- switching asr challenges for low resource indian languages. InInterspeech 2021: The 22nd Annual Conference of the International Speech Communication Association, pages 2881–2885. International Speech Communication Associa- tion. Wessel Kraaij, Thomas Hain, Mike Lincoln, and Wilfried Post. 2005. The ami meeting co...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  17. [17]

    Danni Liu, Thai Binh Nguyen, Sai Koneru, EnesYavuzUgan,Ngoc-QuanPham,Tuan-Nam Nguyen, Tu Anh Dinh, Carlos Mullov, Alexander Waibel, andJanNiehues.2023

    Kit’s low-resource speech translation sys- tems for iwslt2025: System enhancement with synthetic data and model regularization.arXiv preprint arXiv:2505.19679. Danni Liu, Thai Binh Nguyen, Sai Koneru, EnesYavuzUgan,Ngoc-QuanPham,Tuan-Nam Nguyen, Tu Anh Dinh, Carlos Mullov, Alexander Waibel, andJanNiehues.2023. Kit’smultilingual speech translation system f...

    work page arXiv 2023

  18. [18]

    InInternationalConferenceon Machine Learning, pages 28492–28518

    Robust speech recognition via large-scale weaksupervision. InInternationalConferenceon Machine Learning, pages 28492–28518. PMLR. Nathaniel Romney Robinson, Niyati Bafna, Xiluo He, Tom Lupicki, Lavanya Shankar, Cihan Xiao, Qi Sun, Kenton Murray, and David Yarowsky

    work page

  19. [19]

    InProceedings of the 22nd Interna- tional Conference on Spoken Language Transla- tion (IWSLT 2025), pages 315–323

    Jhu iwslt 2025 low-resource system de- scription. InProceedings of the 22nd Interna- tional Conference on Spoken Language Transla- tion (IWSLT 2025), pages 315–323. Jiatong Shi, Shih-Heng Wang, William Chen, Mar- tijn Bartelds, Vanya Bannihatti Kumar, Jinchuan Tian, Xuankai Chang, Dan Jurafsky, Karen Livescu, Hung-yi Lee, et al. 2024. Ml-superb 2.0: Bench...

    work page arXiv 2025