arxiv: 2604.11269 · v1 · submitted 2026-04-13 · 📡 eess.AS

Recognition: unknown

Speaker Attributed Automatic Speech Recognition Using Speech Aware LLMS

Hagai Aronowitz , Zvi Kons , Avihu Dekel , George Saon , Ron Hoory

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:03 UTC · model grok-4.3

classification 📡 eess.AS

keywords speaker-attributed ASRspeech-aware LLMspeaker cluster tagsdata augmentationmulti-speaker transcriptionjoint training

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The pith

A speech-aware LLM adapts to speaker-attributed ASR by jointly training speaker cluster tags with minimal changes.

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

The paper shows that an existing speech-aware large language model can be extended to output transcripts with relative speaker tags such as [Speaker 1]: by adding speaker cluster identification tags like [Speaker 1 cluster 42]: and training them together with the transcription task. Only small architectural adjustments are needed, while data scarcity is handled by artificially concatenating single-speaker recordings into multi-speaker examples. This integrated method is tested on several benchmarks and produces higher accuracy than the standard practice of running speaker diarization first and then ASR. A reader would care because it collapses two separate error-prone stages into one model that directly attributes speech to speakers.

Core claim

By introducing speaker cluster identification tags and training them jointly with speaker-attributed ASR, along with the use of artificially concatenated multi-speaker conversations for data augmentation, the adapted model achieves superior performance compared to conventional pipelines that perform speaker diarization followed by ASR.

What carries the argument

Speaker cluster identification tags that are jointly trained with the SAA task to carry speaker attribution information directly inside the generated transcript.

Load-bearing premise

Artificially concatenated single-speaker recordings can stand in for the acoustic and conversational realities of actual multi-speaker speech when training the model.

What would settle it

Running the adapted model on a large set of naturally recorded multi-speaker conversations with overlaps and measuring whether its speaker-attributed word error rate still beats a strong sequential diarization-plus-ASR baseline; failure to outperform would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2604.11269 by Avihu Dekel, George Saon, Hagai Aronowitz, Ron Hoory, Zvi Kons.

**Figure 1.** Figure 1: Architecture of the Granite-speech model. Modules marked with a fire symbol are trainable during our finetuning process, while those marked with an ice symbol remain frozen. preventing full exploitation of the training data. One approach, as described in Section 4, addresses this issue by augmenting the dataset with artificially constructed conversations assembled from existing speaker turns. In this sect… view at source ↗

**Figure 2.** Figure 2: WER and WDER as a function of the selected encoder [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

read the original abstract

Speaker-Attributed Automatic Speech Recognition (SAA) enhances traditional ASR systems by incorporating relative speaker identity tags directly into the transcript (e.g., [Speaker 1]:, [Speaker 2]:). In this work, we extend the capabilities of Granite-speech, a state-of-the-art speech-aware Large Language Model (LLM) originally trained for transcription and translation. We demonstrate that it can be effectively adapted for SAA with only minimal architectural changes. Our core contribution is the introduction of speaker cluster identification tags (e.g., [Speaker 1 cluster 42]:) which are jointly trained with SAA to significantly improve accuracy. To address limitations in training data, we propose a data augmentation method that uses artificially concatenated multi-speaker conversations. Our approach is evaluated across multiple benchmarks and shows superior performance compared to conventional pipelines that sequentially perform speaker diarization followed by ASR.

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 adds jointly trained speaker cluster tags to a speech LLM for direct SAA output and uses concatenated single-speaker data for training, but the abstract gives no numbers or baseline details to support the superiority claim.

read the letter

The main thing to know is that this work adapts Granite-speech for speaker-attributed ASR by adding speaker cluster identification tags that are trained jointly with the transcription task, plus a simple concatenation method to create multi-speaker training examples from single-speaker recordings. That specific combination of tags and augmentation is not in the prior work they cite, so it counts as a concrete new mechanism rather than just another fine-tune. They keep the architectural changes minimal, which is practical if someone already has a speech-aware LLM running and wants to add attribution without rebuilding the whole pipeline. The cluster tags seem like a reasonable way to let the model maintain speaker identity across turns inside the same forward pass. The soft spot is the evaluation. The abstract states that the method beats sequential diarization-plus-ASR pipelines on multiple benchmarks, yet it supplies no numbers, no error bars, no description of how the baselines were reimplemented, and no information on whether the test sets contain real overlaps or just similar synthetic mixtures. The training data itself is artificially concatenated single-speaker clips, which by design leaves out simultaneous speech, natural prosody shifts, and room acoustics that matter in actual meetings. If the benchmarks share that same limitation, any reported gains could be tied to the training distribution rather than a general improvement. This paper is aimed at applied researchers or engineers who build meeting transcription or multi-user voice systems and are already working with LLM-based ASR. A reader who wants a lightweight way to fold attribution into an existing model could pick up the tag idea and the augmentation trick, but only after seeing the full experimental section. I would send it to peer review because the core technique is straightforward, the problem is relevant, and referees can check whether the numbers and comparisons actually hold up once the details are provided.

Referee Report

2 major / 2 minor

Summary. The paper extends the Granite-speech LLM for Speaker-Attributed ASR (SAA) by introducing speaker cluster identification tags (e.g., [Speaker 1 cluster 42]:) that are jointly trained with the transcription task. It proposes data augmentation via artificial concatenation of single-speaker recordings to create multi-speaker training data and claims that the resulting model outperforms conventional pipelines that perform speaker diarization followed by ASR on multiple benchmarks.

Significance. If the performance gains hold under rigorous testing, the approach could simplify SAA pipelines by integrating speaker attribution directly into a speech-aware LLM, reducing error propagation from separate diarization stages. The cluster-tag mechanism offers a potentially lightweight way to handle relative speaker identities without explicit clustering at inference.

major comments (2)

[Data Augmentation and Training Procedure] The central claim of superior SAA performance rests on training with artificially concatenated single-speaker recordings. This augmentation omits simultaneous speech, natural turn-taking prosody, and room acoustics that characterize real multi-speaker benchmarks; if the evaluation sets contain these phenomena, the reported gains may not generalize. This assumption is load-bearing for the superiority claim over sequential diarization+ASR.
[Experiments and Results] The abstract asserts superior benchmark performance, yet the provided description contains no quantitative results (e.g., WER, speaker-attributed WER, or diarization error rates), error bars, statistical significance tests, or implementation details for the baselines. Without these, the empirical support for the central claim cannot be assessed.

minor comments (2)

[Abstract and Evaluation] Specify the exact benchmarks used (e.g., AMI, ICSI, or others) and whether they are real or synthetic recordings.
[Speaker Cluster Identification Tags] Clarify how the speaker cluster IDs are assigned during training and inference (e.g., clustering algorithm, number of clusters, handling of unseen speakers).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work extending Granite-speech for speaker-attributed ASR. We address each major comment below and indicate planned revisions to improve the manuscript.

read point-by-point responses

Referee: [Data Augmentation and Training Procedure] The central claim of superior SAA performance rests on training with artificially concatenated single-speaker recordings. This augmentation omits simultaneous speech, natural turn-taking prosody, and room acoustics that characterize real multi-speaker benchmarks; if the evaluation sets contain these phenomena, the reported gains may not generalize. This assumption is load-bearing for the superiority claim over sequential diarization+ASR.

Authors: We acknowledge that concatenating single-speaker recordings does not capture overlapping speech, natural prosody, or complex acoustics typical of real multi-speaker data. This is a practical limitation given the scarcity of large annotated multi-speaker corpora. The cluster-tag approach still provides gains on the evaluated benchmarks by enabling joint training of attribution and transcription. In revision we will add an explicit limitations subsection discussing these gaps and outlining future extensions to overlap-aware data, while retaining the current results as evidence for the method's utility under the stated training regime. revision: partial
Referee: [Experiments and Results] The abstract asserts superior benchmark performance, yet the provided description contains no quantitative results (e.g., WER, speaker-attributed WER, or diarization error rates), error bars, statistical significance tests, or implementation details for the baselines. Without these, the empirical support for the central claim cannot be assessed.

Authors: The full manuscript contains quantitative comparisons in the Experiments section. To address the concern we will revise the paper to prominently display all key metrics (WER, SA-WER), include error bars and significance tests where feasible, expand baseline implementation details, and ensure the abstract and introduction explicitly reference these results for clarity. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical adaptation with independent benchmark evaluation

full rationale

The paper describes a practical extension of an existing speech-aware LLM (Granite-speech) for speaker-attributed ASR. It introduces speaker cluster tags trained jointly with SAA and uses artificial concatenation of single-speaker recordings as data augmentation. Performance is assessed via direct comparison on multiple benchmarks against sequential diarization+ASR baselines. No equations, predictions, or uniqueness claims are present that reduce by construction to fitted parameters or self-citations defined within the work itself. The evaluation remains externally falsifiable on held-out data, satisfying the criteria for a self-contained empirical result with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the pre-trained capabilities of Granite-speech, the representativeness of artificial concatenations for real multi-speaker data, and the assumption that minimal architectural changes suffice for effective adaptation.

axioms (1)

domain assumption Artificially concatenated single-speaker recordings are representative of real multi-speaker acoustic and conversational dynamics
Invoked to address training-data limitations for SAA.

invented entities (1)

speaker cluster identification tags no independent evidence
purpose: Jointly trained labels to improve speaker attribution accuracy within the LLM output
Core contribution introduced to enable SAA with minimal model changes

pith-pipeline@v0.9.0 · 5454 in / 1173 out tokens · 66526 ms · 2026-05-10T15:03:55.665604+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Despite these successes, conventional ASR systems are limited to transcribing what was said, without identifying who said it

INTRODUCTION Automatic Speech Recognition (ASR) has witnessed remarkable advancements in recent years, driven by large-scale pretraining and powerful neural sequence model s. Despite these successes, conventional ASR systems are limited to transcribing what was said, without identifying who said it . For real-world tasks such as meeting transcription, con...
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SPEAKER ATTRIBUTED ASR WITH SPEECH- AWARE LLMS 2.1. General framework In speech-aware LLMs, speech input is encoded and projected into the text LLM’s embedding space , thus allowing the LLM to process both speech and text within a single, unified architecture. Our model is built upon Granite -speech-v3.3-8B [9], a speech-aware extension of the Granite-3.3...
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EXPLICIT SPEAKER IDENTIFICATION FOR IMPROVED SAA Training the LLM with relative speaker tags (such as [Speaker 1]) has a limitation : the model’s learning is constrained to differentiating speakers only within individual conversations, Fig. 1. Architecture of the Granite-speech model. Modules marked with a fire symbol are trainable during our fine - tunin...
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Partially overlapping utterances are handled by arranging the transcriptions sequentially. AMI-SDM [18] is a multi -speaker meeting corpus frequently used for SD tasks. In our work, we use audio recorded from a single far -field microphone (Mic #1) and apply the same segmentation and preprocessing procedure described above . Similarly to Fisher and CH, we...
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