arxiv: 2605.12242 · v1 · submitted 2026-05-12 · 💻 cs.CL · cs.AI

Recognition: no theorem link

Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs

Deepak Kumar , Baban Gain , Asif Ekbal

Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:17 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords disfluency correctionmultilingual ASRinstruction tuningcontrastive learningspeech transcriptsLLM fine-tuningsequence tagging

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The pith

A sequence tagger marks disfluencies to guide LLM rewriting of speech transcripts into fluent text across languages.

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

The paper tries to establish that marking disfluent tokens with a sequence tagger and then using those marks to instruction-tune an LLM, plus a contrastive penalty against repeating the disfluencies, produces more readable and grammatically sound corrections than removal alone. This matters for speech-driven systems because fillers, repetitions, and false starts in ASR output break downstream tasks like chatbots and voice assistants. The authors test the pipeline on Hindi, Bengali, and Marathi transcripts and report gains over strong multilingual baselines. They argue that detection-only approaches often leave incomplete or unnatural sentences, while the combined tuning approach preserves meaning better.

Core claim

The authors claim that feeding token-level disfluency signals from a sequence tagger into an instruction-tuned LLM, reinforced by a contrastive objective that penalizes reproduction of marked tokens, yields fluent rewrites of ASR transcripts that keep grammatical structure and semantics intact, with measurable gains on three Indian languages over sequence-to-sequence baselines.

What carries the argument

The disfluency-aware tuning pipeline: a sequence tagger identifies disfluent tokens, those marks condition instruction fine-tuning of the LLM for rewriting, and a contrastive loss term penalizes the model for regenerating the tagged disfluencies.

If this is right

Detection-only removal of disfluencies is insufficient because it often disrupts sentence structure.
Token-level cues combined with contrastive learning let the LLM remove artifacts while keeping intended content.
The method delivers consistent gains on Hindi, Bengali, and Marathi over multilingual sequence-to-sequence models.
Instruction tuning guided by explicit disfluency marks scales better than classical detection pipelines for speech correction.

Where Pith is reading between the lines

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

The approach might transfer to other noisy input correction tasks such as cleaning transcribed meetings or interviews.
If the tagger errs on borderline cases, the LLM's rewriting capacity could still recover fluency in many instances.
Voice-assistant pipelines that feed ASR output directly to chat models would see fewer follow-up clarification requests after such preprocessing.

Load-bearing premise

The initial sequence tagger must correctly identify disfluent tokens, and the contrastive penalty must remove them without distorting the original meaning or grammar in the LLM rewrite.

What would settle it

If the tuned model produces rewrites that lose original meaning or introduce new grammatical errors at higher rates than a plain instruction-tuned LLM without the tagger and contrastive term, on the same test transcripts.

Figures

Figures reproduced from arXiv: 2605.12242 by Asif Ekbal, Baban Gain, Deepak Kumar.

**Figure 1.** Figure 1: Disfluency correction pipeline. A potentially noisy speech transcription is first generated by an ASR [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Flow diagram of the proposed multilingual [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Qualitative comparison of ASR behavior on disfluent speech across Hindi, Bengali, and Marathi. Qwen2- [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

read the original abstract

Automatic Speech Recognition (ASR) transcripts often contain disfluencies, such as fillers, repetitions, and false starts, which reduce readability and hinder downstream applications like chatbots and voice assistants. If left unaddressed, such disfluencies can significantly degrade the reliability of downstream systems. Most existing approaches rely on classical models that focus on identifying disfluent tokens for removal. While this strategy is effective to some extent, it often disrupts grammatical structure and semantic coherence, leading to incomplete or unnatural sentences. Recent literature explored the use of large language models (LLMs); however, these efforts have primarily focused on disfluency detection or data augmentation, rather than performing comprehensive correction. We propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens, and these signals guide instruction fine-tuning of an LLM to rewrite transcripts into fluent text. To further improve reliability, we add a contrastive learning objective that penalizes the reproduction of disfluent tokens, encouraging the model to preserve grammar and meaning while removing disfluent artifacts. Our experiments across three Indian languages, namely Hindi, Bengali, and Marathi show consistent improvements over strong baselines, including multilingual sequence-to-sequence models. These results highlight that detection-only strategies are insufficient. Combining token-level cues with instruction tuning and contrastive learning provides a practical and scalable solution for multilingual disfluency correction in speech-driven NLP systems. We make the codes publicly available at https://github.com/deepak-kumar-98/Mind-the-Pause.

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.

Desk Editor's Note private letter to a colleague

The paper's main move is feeding sequence-tagger labels into LLM instruction tuning plus a contrastive penalty to clean disfluencies in three Indian languages, but the abstract gives no numbers or ablations to show it works.

read the letter

The core idea is straightforward: run a tagger to mark fillers, repeats, and false starts, then use those marks during instruction tuning of an LLM so the model learns to rewrite the transcript without the bad parts. They add a contrastive loss that penalizes the model for regenerating the tagged tokens. This is tested on Hindi, Bengali, and Marathi ASR output, with code released on GitHub. What stands out is the attempt to keep the rewrite from just deleting tokens and breaking grammar, which is a real issue with simple removal baselines. The multilingual angle for these languages is also practical for voice apps in that region. The setup is clear and the public code helps anyone who wants to reproduce or extend it. The soft spots are bigger than minor. The abstract claims consistent gains over seq2seq baselines but shows zero metrics, no tagger accuracy figures, no ablation isolating the contrastive term, and no breakdown by disfluency type or language. Without those, it's impossible to tell if the tagger is reliable enough or if the contrastive penalty is actually preserving meaning instead of just making the model more aggressive at deletion. Only three languages limits the scalability claim. This is for engineers working on ASR post-processing for Indic languages who need fluent text for chatbots or assistants. If the full experiments include solid numbers, ablations, and error analysis, it deserves a serious referee to check whether the pipeline holds up. Otherwise the central claim stays unproven.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a multilingual pipeline for correcting disfluencies (fillers, repetitions, false starts) in ASR transcripts for Hindi, Bengali, and Marathi. A sequence tagger first produces token-level disfluency labels; these labels then guide instruction fine-tuning of an LLM, augmented by a contrastive learning objective that penalizes regeneration of tagged disfluent tokens. The authors claim this yields consistent improvements over strong baselines such as multilingual sequence-to-sequence models and release the code at https://github.com/deepak-kumar-98/Mind-the-Pause.

Significance. If the empirical results hold, the work demonstrates a practical, scalable approach to disfluency correction that integrates token-level cues with LLM rewriting and contrastive penalties, addressing shortcomings of detection-only or pure generation methods for low-resource languages. The public code release is a clear strength that supports reproducibility.

major comments (3)

[Abstract and Experimental Results] Abstract and Experimental Results: The claim of 'consistent improvements over strong baselines' is presented without any quantitative metrics (e.g., BLEU, WER, or human fluency scores), ablation results, or error analysis. This absence prevents assessment of whether the headline gains are substantive or artifacts of the pipeline.
[Method] Method section: The pipeline's first stage is a sequence tagger whose labels drive all subsequent tuning. No precision, recall, or F1 metrics are reported for this tagger, nor any analysis of label noise by disfluency type or language. If tagger accuracy is low, the instruction-tuning and contrastive stages rest on unreliable supervision.
[Experiments] Experiments section: No ablation isolates the contrastive penalty from plain instruction tuning. Without this, it is impossible to confirm that the contrastive term suppresses disfluencies without forcing deletion or alteration of fluent content, which is a load-bearing assumption for the central claim.

minor comments (2)

[Method] The contrastive objective would benefit from an explicit equation defining the penalty term and its weighting relative to the language-modeling loss.
[Results] Tables reporting results should include per-language breakdowns and statistical significance tests to support the 'consistent improvements' claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and describe the revisions we will incorporate.

read point-by-point responses

Referee: [Abstract and Experimental Results] Abstract and Experimental Results: The claim of 'consistent improvements over strong baselines' is presented without any quantitative metrics (e.g., BLEU, WER, or human fluency scores), ablation results, or error analysis. This absence prevents assessment of whether the headline gains are substantive or artifacts of the pipeline.

Authors: We agree that the abstract would be strengthened by including specific metrics and that the experimental presentation requires more ablations and error analysis. In the revised manuscript we will update the abstract to report key quantitative results (BLEU, WER, and human fluency scores) and expand the Experiments section with additional ablation tables and error analysis to substantiate the claimed improvements. revision: yes
Referee: [Method] Method section: The pipeline's first stage is a sequence tagger whose labels drive all subsequent tuning. No precision, recall, or F1 metrics are reported for this tagger, nor any analysis of label noise by disfluency type or language. If tagger accuracy is low, the instruction-tuning and contrastive stages rest on unreliable supervision.

Authors: We acknowledge that explicit performance metrics for the sequence tagger are missing. We will add a dedicated subsection in the Method section reporting precision, recall, and F1 scores per language and per disfluency type, together with an analysis of label noise and its downstream effects. revision: yes
Referee: [Experiments] Experiments section: No ablation isolates the contrastive penalty from plain instruction tuning. Without this, it is impossible to confirm that the contrastive term suppresses disfluencies without forcing deletion or alteration of fluent content, which is a load-bearing assumption for the central claim.

Authors: We agree that an ablation isolating the contrastive objective is necessary. We will add results comparing instruction tuning alone versus the full model with the contrastive penalty, showing its specific contribution to disfluency suppression while preserving fluent content. revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical pipeline with external experimental validation

full rationale

The paper describes a practical pipeline: a sequence tagger produces disfluency labels that guide LLM instruction tuning plus a contrastive penalty. No equations, derivations, or self-referential fittings appear. Claims rest on reported improvements over baselines across Hindi, Bengali, and Marathi, not on any internal definition or self-citation chain that reduces the result to its inputs. This matches the default case of a self-contained empirical study against external benchmarks, so no steps qualify under the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The method assumes standard supervised fine-tuning and contrastive learning will generalize from tagged data to fluent rewrites; no new entities or ad-hoc constants are introduced beyond typical LLM hyperparameters.

axioms (2)

domain assumption Sequence tagger outputs provide reliable guidance for LLM correction without introducing new errors
Invoked in the pipeline description where tagged signals guide fine-tuning.
domain assumption Contrastive objective penalizes disfluent reproduction while preserving semantics
Central to the proposed objective tuning step.

pith-pipeline@v0.9.0 · 5572 in / 1157 out tokens · 30592 ms · 2026-05-13T05:17:04.266276+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · 1 internal anchor

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