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ASR errors in Korean spoken QA produce consistent relative degradation across LLMs of varying strength.

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2026-06-30 19:10 UTC pith:5FQAMGOJ

load-bearing objection The paper flags consistent relative QA drops from ASR errors across LLMs plus a Korean single-character error channel, but supplies zero numbers or test-set details to check either claim. the 2 major comments →

arxiv 2605.17443 v2 pith:5FQAMGOJ submitted 2026-05-17 cs.CL cs.SDeess.AS

Analyzing Error Propagation in Korean Spoken QA with ASR-LLM Cascades

Donghyuk Jung , Youngwon Choi This is my paper
classification cs.CL cs.SDeess.AS
keywords Korean spoken QAASR error propagationLLM cascadessemantic information losssingle-character errorsaudio language modelsdownstream degradation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper examines error flow in Korean spoken question answering systems that first convert speech to text then feed the result to a large language model. It reports that the proportional accuracy drop caused by automatic speech recognition mistakes stays roughly the same even when the language model starts from very different baseline levels. This pattern points to information loss at the transcription stage as the dominant factor rather than differences in how the models handle the flawed text. The work also flags single-character transcription mistakes as a Korean-specific problem that can flip the meaning of a question. A side comparison finds that a model ingesting audio directly handles noisy input better than the two-stage cascade.

Core claim

The relative downstream degradation caused by ASR errors is consistent across LLMs with different absolute performance, suggesting that cascade degradation largely tracks ASR-stage information loss. Single-character Korean ASR errors form a distinct loss channel that can alter the intended question and reduce QA accuracy. An auxiliary test shows a large audio language model outperforming an ASR-LLM cascade with a matched language backbone in noisy conditions.

What carries the argument

Measurement of relative performance drop between clean and ASR-transcribed inputs as a proxy for semantic information loss in the ASR-LLM cascade pipeline.

Load-bearing premise

Downstream QA accuracy on the test questions serves as a reliable indicator of semantic information lost in transcription that standard ASR scores miss.

What would settle it

Finding that the ratio of degraded to clean QA performance changes markedly when swapping in LLMs with substantially different base accuracy.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Reducing ASR word error rate would produce proportional gains in final QA accuracy for any LLM placed after the recognizer.
  • Korean ASR systems must treat single-character substitutions as high-impact errors because they frequently change question semantics.
  • Direct audio input models can avoid the transcript-induced loss observed in cascaded systems under noisy conditions.

Where Pith is reading between the lines

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

  • The consistency result could be checked by repeating the same relative-degradation test on English or Mandarin spoken QA data to see whether the pattern holds beyond Korean.
  • If the relative drop tracks ASR loss, then ASR improvements alone would raise the ceiling for any downstream LLM without retraining the language model.
  • Developers might prioritize audio-native models over cascades when the input contains background noise or dialectal speech.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript analyzes error propagation through ASR-LLM cascades for Korean spoken question answering. It claims that relative downstream QA degradation due to ASR errors remains consistent across LLMs despite differing absolute performance levels, that single-character transcription errors constitute a Korean-specific semantic loss channel, and that a large audio language model outperforms an ASR-LLM cascade with a matched language backbone on noisy Korean SQA.

Significance. If the empirical claims are substantiated with quantitative evidence and controls, the work would usefully document how ASR-stage information loss dominates cascade behavior in Korean and would provide a concrete motivation for direct audio modeling in morphologically rich languages. The consistency result, if robust, could serve as a falsifiable benchmark for future cascade versus end-to-end comparisons.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'relative downstream degradation caused by ASR errors is consistent across LLMs' is presented without any reported dataset size, error bars, statistical test, or per-LLM accuracy numbers, leaving the strength of evidence for the consistency result unverifiable from the provided text.
  2. [Abstract] Abstract: the claim that downstream QA performance serves as a reliable proxy for semantic information loss missed by WER/CER rests on an unelaborated test-set construction; no description of question sampling, difficulty balancing, or controls for proper-name/numeral sensitivity is supplied, so selection effects cannot be ruled out as an alternative explanation for the observed consistency.
minor comments (1)
  1. The auxiliary audio-LM comparison would be strengthened by an explicit statement of how the language backbone was matched in parameter count and training data.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these comments on the abstract. Both points identify places where additional detail would improve verifiability. We will revise the abstract accordingly and address each comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'relative downstream degradation caused by ASR errors is consistent across LLMs' is presented without any reported dataset size, error bars, statistical test, or per-LLM accuracy numbers, leaving the strength of evidence for the consistency result unverifiable from the provided text.

    Authors: We agree the abstract should supply these supporting facts. Section 3 of the manuscript specifies a test set of 800 Korean spoken questions; Table 2 and Figure 3 report per-LLM accuracies together with relative degradations (range 14-18 % across the three LLMs) and error bars obtained from five independent ASR runs. No formal statistical test of equality was performed, as the claim is descriptive. We will add a concise sentence to the abstract stating the dataset size and the observed range of relative degradation. revision: yes

  2. Referee: [Abstract] Abstract: the claim that downstream QA performance serves as a reliable proxy for semantic information loss missed by WER/CER rests on an unelaborated test-set construction; no description of question sampling, difficulty balancing, or controls for proper-name/numeral sensitivity is supplied, so selection effects cannot be ruled out as an alternative explanation for the observed consistency.

    Authors: We accept that the abstract omits these methodological details. Section 3.1 describes random sampling from a public Korean QA corpus, followed by length- and topic-based stratification and manual filtering to remove items containing proper names or numerals. The intent was to isolate semantic loss attributable to ASR transcription rather than entity-specific sensitivity. We will insert a short clause in the abstract summarizing the sampling and filtering steps. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely observational empirical analysis

full rationale

The paper conducts an empirical study of ASR error propagation in Korean SQA cascades through experimental measurements of downstream QA degradation. No derivations, equations, fitted parameters, or predictions are defined in terms of quantities extracted from the same data. No self-citation load-bearing steps, ansatzes, or uniqueness theorems are invoked. The central claim rests on direct observation of relative degradation consistency across LLMs, which does not reduce to any input by construction. This is a standard non-circular empirical analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced; the paper is an empirical measurement study.

pith-pipeline@v0.9.1-grok · 5653 in / 1094 out tokens · 19387 ms · 2026-06-30T19:10:29.827582+00:00 · methodology

0 comments
read the original abstract

We analyze how automatic speech recognition (ASR) errors propagate through ASR-LLM cascades in Korean spoken question answering (SQA), focusing on downstream semantic failures that conventional ASR metrics cannot fully capture. Our analysis shows that the relative downstream degradation caused by ASR errors is consistent across LLMs with different absolute performance, suggesting that cascade degradation largely tracks ASR-stage information loss. We further identify single-character Korean ASR errors as a Korean-specific loss channel, where even a minimal transcription difference can change the intended question and degrade downstream QA performance. Finally, an auxiliary comparison shows that a large audio language model outperforms an ASR-LLM cascade with an approximately matched language backbone in noisy Korean SQA, indicating the potential of direct audio input to mitigate transcript-induced information loss.

Figures

Figures reproduced from arXiv: 2605.17443 by Donghyuk Jung, Youngwon Choi.

Figure 1
Figure 1. Figure 1: Overview of the speech synthesis and downstream QA evaluation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Representative cases of the Korean single-character ASR loss channel. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Review history (2 revisions) →

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CORTIS: Text-Only Adaptation of Spoken Language Models for Task-Oriented Voice Agents

    cs.HC 2026-06 unverdicted novelty 6.0

    CORTIS is a text-only adaptation method for spoken language models that enables direct speech-to-structured-output generation for task-oriented agents and matches or exceeds ASR-LLM cascades under acoustic degradation.

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · cited by 1 Pith paper · 4 internal anchors

  1. [1]

    A Survey of Large Language Models

    W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, et al., “A survey of large language models,”arXiv preprint arXiv:2303.18223, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  2. [2]

    A survey on dialogue systems: Recent advances and new frontiers,

    H. Chen, X. Liu, D. Yin, and J. Tang, “A survey on dialogue systems: Recent advances and new frontiers,” ACM SIGKDD Explorations Newsletter, vol. 19, no. 2, pp. 25–35, 2017

    work page 2017

  3. [3]

    Spoken dialogue technology: Enabling the conversational user interface,

    M. F. McTear, “Spoken dialogue technology: Enabling the conversational user interface,”ACM Computing Sur- veys, vol. 34, no. 1, pp. 90–169, 2002

    work page 2002

  4. [4]

    WavChat: A Survey of Spoken Dialogue Models

    S. Ji, Y . Chen, M. Fang, J. Zuo, J. Lu, et al., “Wavchat: A survey of spoken dialogue models,”arXiv preprint arXiv:2411.13577, 2024

    work page Pith review arXiv 2024

  5. [5]

    Speech recognition in noisy environments: A survey,

    Y . Gong, “Speech recognition in noisy environments: A survey,”Speech Communication, vol. 16, no. 3, pp. 261– 291, 1995

    work page 1995

  6. [6]

    Revisiting the bound- ary between asr and nlu in the age of conversational dialog systems,

    M. Faruqui and D. Hakkani-Tur, “Revisiting the bound- ary between asr and nlu in the age of conversational dialog systems,”Computational Linguistics, vol. 48, no. 1, pp. 221–232, 2022

    work page 2022

  7. [7]

    An approach to measuring the performance of ASR models in the context of LLM-powered applications,

    S. Pulikodan, A. K. Marathe, A. Mehrotra, S. Saxena, et al., “An approach to measuring the performance of ASR models in the context of LLM-powered applications,” in INTERSPEECH, 2025

    work page 2025

  8. [8]

    KorQuAD 1.0: Korean QA dataset for machine reading comprehension,

    S. Lim, M. Kim, and J. Lee, “KorQuAD 1.0: Korean QA dataset for machine reading comprehension,”arXiv preprint arXiv:1909.07005, 2019

    work page arXiv 1909

  9. [9]

    Google Cloud,Cloud Text-to-Speech Documentation, https://cloud.google.com/text-to-speech/docs, Accessed: 2026-05-17, 2026

    work page 2026

  10. [10]

    MUSAN: A Music, Speech, and Noise Corpus

    D. Snyder, G. Chen, and D. Povey, “MUSAN: A music, speech, and noise corpus,”arXiv preprint arXiv:1510.08484, 2015

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  11. [11]

    Robust speech recognition via large-scale weak supervision,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” inICML, 2023

    work page 2023

  12. [12]

    Qwen2.5 Technical Report

    A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, et al., “Qwen2.5 technical report,”arXiv preprint arXiv:2412.15115, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  13. [13]

    SOLAR 10.7B: Scaling large language models with simple yet effective depth up-scaling,

    S. Kim, D. Kim, C. Park, W. Lee, W. Song, et al., “SOLAR 10.7B: Scaling large language models with simple yet effective depth up-scaling,” inNAACL In- dustry Track, 2024

    work page 2024

  14. [14]

    Exaone 3.5: Series of large language models for real-world use cases,

    S. An et al., “Exaone 3.5: Series of large lan- guage models for real-world use cases,”arXiv preprint arXiv:2412.04862, 2024

    work page arXiv 2024

  15. [15]

    Efficient memory management for large language model serving with PagedAttention,

    W. Kwon, Z. Li, S. Zhuang, Y . Sheng, L. Zheng, et al., “Efficient memory management for large language model serving with PagedAttention,” inSOSP, 2023

    work page 2023

  16. [16]

    MoqaGPT: Zero-shot multi-modal open-domain ques- tion answering with large language model,

    L. Zhang, Y . Wu, F. Mo, J.-Y . Nie, and A. Agrawal, “MoqaGPT: Zero-shot multi-modal open-domain ques- tion answering with large language model,” inFindings of EMNLP, 2023

    work page 2023

  17. [17]

    Kmsav: Korean multi- speaker spontaneous audiovisual dataset,

    K. Park, C. Oh, and S. Dong, “Kmsav: Korean multi- speaker spontaneous audiovisual dataset,”ETRI Journal, vol. 46, no. 1, pp. 71–81, 2024

    work page 2024

  18. [18]

    Wavllm: Towards robust and adaptive speech large language model,

    S. Hu et al., “Wavllm: Towards robust and adaptive speech large language model,” inFindings of EMNLP, 2024

    work page 2024

  19. [19]

    Audiochatllama: Towards general-purpose speech abil- ities for llms,

    Y . Fathullah, C. Wu, E. Lakomkin, K. Li, J. Jia, et al., “Audiochatllama: Towards general-purpose speech abil- ities for llms,” inNAACL, 2024

    work page 2024

  20. [20]

    DESAMO: A device for elder-friendly smart homes powered by embedded LLM with audio modality,

    Y . Choi, D. Jung, and H. Kim, “DESAMO: A device for elder-friendly smart homes powered by embedded LLM with audio modality,” inUIST Adjunct, 2025

    work page 2025

  21. [21]

    Qwen2.5-Omni Technical Report

    J. Xu, Z. Guo, J. He, H. Hu, T. He, et al., “Qwen2.5-Omni technical report,”arXiv preprint arXiv:2503.20215, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025