arxiv: 2605.05231 · v1 · submitted 2026-04-24 · 📡 eess.AS · cs.SD

Recognition: unknown

Prompting Whisper for Joint Speech Transcription and Diarization

Henk van den Heuvel, Mariia Zamyrova

Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:56 UTC · model grok-4.3

classification 📡 eess.AS cs.SD

keywords Whisper modelspeaker diarizationspeech transcriptionfine-tuningprompt engineeringSOT formatlong-form audiomedical conversations

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The pith

Fine-tuning Whisper with speaker-labelled prompts yields consistent speaker IDs and better transcriptions for long audio.

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

The paper explores integrating speaker diarization into Whisper's transcription process for Dutch medical dialogues. Researchers prompt the model with text that includes speaker labels and then fine-tune it to output transcriptions in a serialized format similar to SOT. This results in more consistent speaker assignments when handling long recordings split into chunks, along with improved accuracy in the spoken words themselves. A reader would care because it suggests a streamlined approach to two tasks that typically require separate tools, which could simplify real-time applications like transcribing doctor-patient conversations.

Core claim

Fine-tuning Whisper with speaker-labelled prompts enables it to insert speaker labels into transcriptions with promising accuracy, producing more consistent speaker IDs across chunks of long-form audio and improving verbatim transcription, although performance suffers from propagating mistakes in prompts and inaccurate timestamps on overlapping speech.

What carries the argument

Speaker-labelled prompts for fine-tuning Whisper to generate SOT-style output that combines transcription and diarization in one sequence.

If this is right

Consistent speaker IDs across audio chunks enable reliable diarization for extended conversations.
Improved verbatim transcription supports higher-quality records in applications like medical consultations.
Error propagation through prompt chains becomes a key issue to address in future iterations.
The approach identifies limitations in handling overlapping speech without additional mechanisms.

Where Pith is reading between the lines

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

This integrated method might eliminate the need for separate diarization pipelines in speech processing systems.
Applying it to other languages or domains could test its generalizability beyond Dutch medical speech.
Developing mechanisms to correct prompt errors or improve overlap timestamping would be a natural next step.
Real-time deployment could be feasible if chunk consistency holds in live streaming scenarios.

Load-bearing premise

That errors in speaker labels will not propagate through the prompt chain and that Whisper can assign accurate timestamps to overlapping speech without additional mechanisms.

What would settle it

Running the fine-tuned model on long audio with known speaker changes and overlaps, then checking if speaker IDs stay consistent across chunks and if overlap timestamps match manual annotations.

read the original abstract

As part of the MediSpeech project, we aim to develop a system that transcribes and diarizes Dutch conversations between doctors and patients in real-time. In this research (in-progress) we explore ways of efficiently combining Whisper with speaker diarization (SD). After trying to prompt Whisper with text that contains speaker labels, we observed that it is able to insert labels into the transcription with promising accuracy. We continued this line of research by fine-tuning Whisper with speaker-labelled prompts to generate transcriptions in a format similar to that of Serialized Output Training (SOT). Fine-tuning Whisper yielded more consistent speaker IDs across the chunks of long-form audio and improved verbatim transcription. The study uncovered new challenges as Whisper's SD performance suffers because of mistakes that get propagated through prompts and inaccurate timestamps assigned to overlapping speech.

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

Early exploration of Whisper prompting for speaker-labeled output in Dutch medical speech is honest about its limits but too thin on evidence to evaluate.

read the letter

The main point on this paper is that it describes an in-progress attempt to get Whisper to handle both transcription and speaker diarization in one pass for Dutch doctor-patient conversations. They start with prompting that includes speaker labels, then fine-tune the model to produce SOT-style output with speaker tags. The authors note that fine-tuning gave more stable speaker IDs across audio chunks and better verbatim accuracy, but they also flag two clear problems: errors in the prompt chain propagate forward, and timestamps on overlapping speech stay inaccurate.

Referee Report

2 major / 2 minor

Summary. The manuscript (in-progress) explores combining the Whisper ASR model with speaker diarization for real-time Dutch doctor-patient conversations. It reports that prompting Whisper with speaker-labeled text enables insertion of labels with promising accuracy, and that subsequent fine-tuning on speaker-labeled prompts produces output similar to Serialized Output Training, yielding more consistent speaker IDs across long-form audio chunks and improved verbatim transcription. The authors note two open challenges: error propagation through the prompt chain and inaccurate timestamps on overlapping speech, which degrade SD performance.

Significance. If the qualitative observations are confirmed with quantitative evaluation, the work could demonstrate a lightweight way to perform joint transcription and diarization with a single pre-trained model, reducing the need for separate SD pipelines in domain-specific applications such as medical conversations. At present the contribution is exploratory and its significance is modest because no metrics, baselines, or ablation results are supplied.

major comments (2)

[Abstract] Abstract and experimental description: the claim that fine-tuning 'yielded more consistent speaker IDs across the chunks of long-form audio and improved verbatim transcription' is presented without any quantitative metrics (e.g., speaker error rate, WER, diarization error rate), baselines, or statistical comparisons, rendering the central positive observation unevaluable.
[The study description] The manuscript identifies prompt-chain error propagation and inaccurate timestamps on overlapping speech as factors that 'suffer' SD performance, yet provides no mitigation strategy or measurement of their impact; these issues directly affect the reliability of the proposed joint approach and must be addressed for the method to be viable.

minor comments (2)

Clarify the chunking procedure for long-form audio, the exact format of the speaker-labeled prompts, and the fine-tuning data (size, annotation method, train/test split).
Add at least one concrete example of an input prompt and the corresponding Whisper output to illustrate the observed behavior.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on this in-progress exploratory study. We agree that quantitative metrics are needed to properly evaluate the observations and will strengthen the manuscript in revision. We address the major comments point by point below.

read point-by-point responses

Referee: [Abstract] Abstract and experimental description: the claim that fine-tuning 'yielded more consistent speaker IDs across the chunks of long-form audio and improved verbatim transcription' is presented without any quantitative metrics (e.g., speaker error rate, WER, diarization error rate), baselines, or statistical comparisons, rendering the central positive observation unevaluable.

Authors: We acknowledge that the abstract presents qualitative observations on improved speaker ID consistency and transcription without supporting quantitative metrics or baselines, which limits evaluability. As the work is preliminary and in-progress, these stem from initial manual inspections of outputs rather than formal evaluation. In the revised manuscript we will add quantitative results, including WER for verbatim transcription accuracy, diarization error rate (DER) or speaker error rate to measure ID consistency across chunks, and comparisons against baselines such as unmodified Whisper and separate ASR+SD pipelines, with appropriate statistical analysis. revision: yes
Referee: [The study description] The manuscript identifies prompt-chain error propagation and inaccurate timestamps on overlapping speech as factors that 'suffer' SD performance, yet provides no mitigation strategy or measurement of their impact; these issues directly affect the reliability of the proposed joint approach and must be addressed for the method to be viable.

Authors: We agree these factors limit reliability and that their impact should be measured. The manuscript currently flags them as open challenges uncovered during the study but does not quantify their contribution or propose mitigations. In revision we will add an error analysis to measure their specific impact on SD performance (e.g., by decomposing errors into propagation vs. overlap categories). We will also describe and evaluate initial mitigation approaches, such as prompt correction loops to reduce propagation and overlap-aware timestamp refinement, to improve viability of the joint method. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript is an in-progress empirical exploration of prompting and fine-tuning Whisper for joint transcription and diarization. It contains no mathematical derivations, equations, fitted parameters, or load-bearing self-citations. Claims rest on direct experimental observations (e.g., improved speaker-ID consistency after fine-tuning), with limitations such as prompt-chain error propagation and overlap timestamp errors explicitly reported as open issues rather than resolved by construction. The work is therefore self-contained against external benchmarks with no reduction of outputs to inputs by definition or self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work relies on the pre-trained Whisper model and the assumption that speaker labels can be treated as ordinary text tokens. No free parameters, new axioms, or invented entities are introduced beyond standard fine-tuning practice.

axioms (1)

domain assumption Whisper can treat speaker labels inserted in prompts as ordinary text tokens that influence output format.
Invoked when the authors prompt Whisper with labelled text and later fine-tune on SOT-style output.

pith-pipeline@v0.9.0 · 5435 in / 1237 out tokens · 72327 ms · 2026-05-09T19:56:46.109289+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 1 canonical work pages

[1]

[Spreker 1] Hallo! [Spreker 2] Hallo! Hoe gaat het met jou?

INTRODUCTION Whisper [1] has established itself as a front -runner when it comes to automatic speech recognition (ASR). A feature that particularly makes Whisper stand out is its prompting functionality. By using text prompts , we can condition Whisper’s transcriptions, which can be helpful for transcribing chunked long-form audio or correcting domain - s...
[2]

Standard Dutch

DATA We make use of the Corpus Gesproken Nederlands (CGN) # speakers Train Valid Test Test subset 2 503 53 121 14 3 106 29 65 4 4 35 10 - - 5 3 - - - Total duration 99.5 12.5 27.0 2.8 Avg duration 6.9 6.6 7.3 8.5 Table 1: For each partition of the CGN comp-A dataset: the number of audio files with N -speakers, the total amount of hours of speech, and the ...
[3]

[Speaker 1] [Sp eaker 2]

METHODS 3.1. Prompting After more experimenting we found that prompting the model with just the speaker labels as hotwords , e.g. , “[Speaker 1] [Sp eaker 2]” , yielded a labelled transcript similar to the one we got with a full sentence prompt while taking up fewer tokens . During training, to further minimize the size of the prompt , we replace the labe...
[4]

xxx”) or laughter ( “ggg

EXPERIMENTAL SETUP 4.1. Model We use Whisper large-v2 for fine -tuning and as our baseline. During tuning we use the Huggingface implementation and modify the model’s ‘config.json’ file by removing all non -special tokens from the ‘suppress_tokens’ list. For long-form evaluation on the test set we use the quantized Faster Whisper implementations of the ba...
[5]

uh”, “oh

RESULTS AND DISCUSSION One of our fi rst observations after fine -tuning was the improvement in overall (verbatim) WER (Table 2). Especially, we found that the rate of filler word recognition increased from 7% hits to 63% hits for top 7 filler words with frequency above 500 over the whole test set (e.g., “uh”, “oh” and “m” ). This aligns with the results ...
[6]

CONCLUSION Fine-tuning Whisper with prompts yielded improved verbatim transcription and generated more uniform speaker labels. Though the model’s ability to preserve speaker labels between audio chunks using prompting is promising, many challenges remain , namely correcting timestamps assigned to overlapping speech and providing audio context for more rob...
[7]

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,” in Proc. of the 40th International Conference on Machine Learning, pp. 28492–28518, 2023

2023
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Prompting Whisper for Improved Verbatim Transcription and End -to-end,

G. D. Smith, D. Yee, J. K. Chen, and L. Findlater, “ Prompting Whisper for Improved Verbatim Transcription and End -to-end,” in Proc. Interspeech 2025, pp. 1943-1947, 2025

2025
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H. Ma, Z. Peng, M. Shao, J. Li, and J. Liu, “Extending Whisper with Prompt Tuning to Target -Speaker ASR,” ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 12516-12520, 2024

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DiCoW: Diarization -Conditioned Whisper for Target Speaker Automatic Speech Recognition,

A. Polok, D. Klement, M. Kocour, J. Han, F. Landini, B. Yusuf, and L. Burget, “DiCoW: Diarization -Conditioned Whisper for Target Speaker Automatic Speech Recognition,” Computer Speech & Language, 2026

2026
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Real-time multilingual speech recognition and speaker diarization system based on Whisper segmentation,

K. -M. Lyu, R. -y. Lyu, and H. -T. Chang, “ Real-time multilingual speech recognition and speaker diarization system based on Whisper segmentation,” PeerJ Computer Science, 2024

2024
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Whisper-TAD: A general model for Transcription, Alignment and Diarization of speech ,

C. Lavigne, and A. Stasica, “Whisper-TAD: A general model for Transcription, Alignment and Diarization of speech ,” in Proc. of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024), pp. 33-38, 2024

2024
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Serialized Output Training for End -to-End Overlapped Speech Recognition,

N. Kanda, Y. Gaur, X. Wang, Z. Meng, and T. Yoshioka, “Serialized Output Training for End -to-End Overlapped Speech Recognition,” in Proc. Interspeech 2020, pp. 2797-2801, 2020

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J. E. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W . Chen, “LoRA: Low -Rank Adaptation of Large Language Models,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2022

2022
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CGN, an annotated corpus of spoken Dutch,

I. Schuurman, M. Schouppe, H. Hoekstra, and T. van der Wouden, “CGN, an annotated corpus of spoken Dutch,” in Proc. of 4th International Workshop on Linguistically Interpreted Corpora (LINC-03) at EACL 2003, 2003

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N. Oostdijk, “ The design of the Spoken Dutch Corpus ,” Language and Computers, vol. 36 (1), pp. 105–112, 2001

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Word Error Rate Definitions and Algorithms for Long - Form Multi -Talker Speech Recognition,

T. von Neumann, C. Boeddeker, M. Delcroix, and R. Haeb - Umbach, “Word Error Rate Definitions and Algorithms for Long - Form Multi -Talker Speech Recognition,” IEEE Transactions on Audio Speech and Language Processing, vol. 33, pp. 3174 –3188, 2025

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[18]

Whisper Has an Internal Word Aligner,

S.-L. Yen, Y. Meng, and H. Ta ng, “Whisper Has an Internal Word Aligner,” arXiv preprint arXiv: 2509.09987, 2025

work page arXiv 2025