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arxiv: 2603.00086 · v2 · pith:7YDD7VVSnew · submitted 2026-02-16 · 💻 cs.CL · cs.AI· cs.SD· eess.AS

Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization

Ambre Marie (LaTIM) , Thomas Bertin (DySoLab) , Guillaume Dardenne (LaTIM) , Gwenol\'e Quellec (LaTIM) This is my paper

Pith reviewed 2026-05-21 12:17 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.SDeess.AS
keywords French clinical transcriptionspeaker diarizationLLM post-processingword diarization error ratemedical speech recognitionmulti-pass architecture
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The pith

A multi-pass LLM architecture alternating speaker and word recognition passes improves WDER in French clinical conversations.

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

The paper tests whether iterative LLM post-processing can fix high error rates in automatic transcription of spontaneous French medical speech. It alternates dedicated passes for identifying speakers and correcting words, then measures the effect on two real clinical datasets. On suicide-prevention telephone calls the method produces statistically significant drops in weighted diarization error rate; on awake neurosurgery consultations the error rate stays stable. The system never fails to produce output and runs at a real-time factor of 0.32, which the authors present as evidence that the approach could be practical for offline clinical use once tested on larger collections.

Core claim

An iterative multi-pass LLM post-processing pipeline that alternates Speaker Recognition and Word Recognition passes reduces weighted diarization error rate on French suicide-prevention counseling transcripts while leaving performance unchanged on awake neurosurgery consultation transcripts, with no output failures and an RTF of 0.32.

What carries the argument

Multi-pass LLM post-processing architecture that alternates Speaker Recognition and Word Recognition passes.

If this is right

  • Statistically significant WDER reduction on suicide-prevention telephone data (p<0.05, n=18)
  • Unchanged WDER on awake neurosurgery consultation data (n=10)
  • Zero output failures across all tested dialogues
  • Real-time factor of 0.32, compatible with offline clinical workflows

Where Pith is reading between the lines

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

  • The same alternating-pass structure could be applied to other languages or medical specialties once larger training sets become available.
  • Combining the post-processor with different base ASR models might yield further gains without retraining the underlying recognizer.
  • Explicit checks for introduced hallucinations or factual drift would strengthen the case for routine clinical use.

Load-bearing premise

Two small clinical datasets and the particular prompting and ordering choices tested are enough to support claims of feasibility for offline clinical deployment.

What would settle it

A larger, more diverse collection of French clinical recordings on which the same multi-pass procedure produces no WDER reduction or introduces new factual errors in the corrected text.

Figures

Figures reproduced from arXiv: 2603.00086 by Ambre Marie (LaTIM), Guillaume Dardenne (LaTIM), Gwenol\'e Quellec (LaTIM), Thomas Bertin (DySoLab).

Figure 1
Figure 1. Figure 1: Representative examples of qualitative improvements. imal, this establishes a stable foundation for iterative refine￾ment strategies explored in subsequent sections. The compara￾ble performance between GPT4omini and Qwen80B suggests that open-source models have reached sufficient capability for medical transcription post-processing tasks, provided that suf￾ficient clinical context is given with structured … view at source ↗
read the original abstract

Automatic speech recognition for French medical conversations remains challenging, with word error rates often exceeding 30% in spontaneous clinical speech. This study proposes a multi-pass LLM post-processing architecture alternating between Speaker Recognition and Word Recognition passes to improve transcription accuracy and speaker attribution. Ablation studies on two French clinical datasets (suicide prevention telephone counseling and preoperative awake neurosurgery consultations) investigate four design choices: model selection, prompting strategy, pass ordering, and iteration depth. Using Qwen3-Next-80B, Wilcoxon signed-rank tests confirm significant WDER reductions on suicide prevention conversations (p<0.05, n=18), while maintaining stability on awake neurosurgery consultations (n=10), with zero output failures and acceptable computational cost (RTF 0.32), suggesting feasibility for offline clinical deployment, pending validation on larger corpora.

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 a multi-pass LLM post-processing architecture that alternates between Speaker Recognition and Word Recognition passes to improve transcription accuracy and speaker diarization for French clinical interviews. Ablation studies examine four design choices (model selection, prompting strategy, pass ordering, and iteration depth) on two small French clinical datasets: suicide prevention telephone counseling (n=18) and preoperative awake neurosurgery consultations (n=10). Using Qwen3-Next-80B, the work reports statistically significant WDER reductions on the first dataset (p<0.05), stability on the second, zero output failures, and RTF of 0.32, concluding that the approach suggests feasibility for offline clinical deployment pending larger validation.

Significance. If the empirical gains hold under larger-scale testing, the method offers a practical, low-failure-rate way to post-process ASR output in French medical conversations where baseline WER often exceeds 30%. The ablation results, Wilcoxon tests, and reported computational cost provide concrete evidence of a workable pipeline that could be deployed offline without retraining ASR models.

major comments (2)
  1. [Abstract] Abstract and Results: The central claim of feasibility for offline clinical deployment rests on Wilcoxon-significant WDER reductions with n=18 (suicide prevention) and n=10 (neurosurgery). These sample sizes are too small to support general deployment recommendations; the observed gains could reflect dataset-specific prompting artifacts rather than robust recovery of ground-truth content.
  2. [Methods] Methods and Ablations: No details are provided on data splits, exact prompt templates, or manual audits for LLM-induced hallucinations or content alterations in the medical corrections. In a clinical setting, silent rewriting of patient statements would undermine the utility of any WDER improvement.
minor comments (2)
  1. WDER and RTF are used without an initial definition or reference to standard formulas; adding these on first use would improve readability.
  2. The description of the alternating pass architecture would benefit from a clearer diagram or pseudocode to show the exact iteration loop and termination criteria.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the detailed review. We appreciate the feedback on sample sizes and methodological details. We respond to each major comment below and indicate where revisions will be made to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Results: The central claim of feasibility for offline clinical deployment rests on Wilcoxon-significant WDER reductions with n=18 (suicide prevention) and n=10 (neurosurgery). These sample sizes are too small to support general deployment recommendations; the observed gains could reflect dataset-specific prompting artifacts rather than robust recovery of ground-truth content.

    Authors: We agree that the small sample sizes limit the generalizability of our findings and that larger validation is essential before recommending deployment. The abstract already qualifies the conclusion with 'suggesting feasibility for offline clinical deployment, pending validation on larger corpora.' To further address this, we will revise the abstract and discussion to more explicitly highlight the preliminary nature of the results and the potential for dataset-specific effects. We maintain that the significant WDER reduction (p<0.05) on the suicide prevention set and stability on the neurosurgery set, using Wilcoxon tests, provide valuable initial evidence for the multi-pass approach in this domain. We will also add more context on why these datasets were chosen and their representativeness within French clinical speech. revision: partial

  2. Referee: [Methods] Methods and Ablations: No details are provided on data splits, exact prompt templates, or manual audits for LLM-induced hallucinations or content alterations in the medical corrections. In a clinical setting, silent rewriting of patient statements would undermine the utility of any WDER improvement.

    Authors: We thank the referee for pointing this out. The original manuscript omitted these details for space reasons, but we will include them in the revision. Specifically: (1) Data splits: The 18 suicide prevention conversations and 10 neurosurgery consultations were used in their entirety for evaluation without train/test splits as this is a post-processing method on fixed ASR outputs; we will clarify this. (2) Exact prompt templates: We will add the full prompts for Speaker Recognition and Word Recognition passes to an appendix. (3) Manual audits: We performed manual reviews of a subset of outputs to check for hallucinations or content changes, finding none that altered medical meaning, and will describe the audit protocol and results. This ensures transparency regarding potential alterations. We agree that preventing silent rewriting is critical in clinical applications and will emphasize how the iterative passes are constrained to recognition tasks rather than generation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results on external datasets

full rationale

The paper reports an empirical evaluation of an LLM post-processing pipeline on two external clinical datasets (n=18 and n=10), using ablation studies over four design choices and Wilcoxon signed-rank tests to measure WDER changes. No equations, fitted parameters, or self-citations are presented that reduce the reported improvements to inputs by construction. The central feasibility claim rests on direct measurement against ground-truth transcriptions rather than any self-definitional or self-citation chain, making the derivation self-contained against the provided benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on empirical testing of an LLM refinement pipeline rather than new theory; design choices like iteration depth and prompting are explored via ablation but treated as tunable without independent theoretical justification.

free parameters (2)
  • iteration depth
    Tested as one of four design choices in ablation studies to optimize performance
  • prompting strategy
    Varied across experiments as a key variable affecting pass effectiveness
axioms (2)
  • domain assumption Large language models can reliably alternate between speaker attribution and word correction tasks in clinical French speech without introducing new errors
    Invoked as the basis for the multi-pass architecture in the abstract
  • domain assumption The two tested clinical conversation types are representative enough to indicate deployment feasibility
    Used to support the conclusion of acceptable RTF and zero failures

pith-pipeline@v0.9.0 · 5699 in / 1550 out tokens · 105229 ms · 2026-05-21T12:17:27.390790+00:00 · methodology

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Reference graph

Works this paper leans on

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