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arxiv: 2606.10838 · v1 · pith:PPPUGFWW · submitted 2026-06-09 · eess.AS

Towards Deep Contextual Reasoning from Broad Descriptions for ASR with Speech-LLM via Metadata-Driven Reasoning Chains

Jakob Poncelet , Hugo Van hamme This is my paper

Reviewed by Pith2026-06-27 11:33 UTCgrok-4.3pith:PPPUGFWWopen to challenge →

classification eess.AS
keywords automatic speech recognitionspeech-LLMcontextual ASRchain-of-thought reasoningnamed entitiesvideo metadatarare word recognition
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The pith

A speech-LLM learns chain-of-thought reasoning over video metadata to correct transcripts of rare words and named entities

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

The paper develops a method to train speech large language models to perform contextual reasoning in automatic speech recognition from broad descriptions such as video metadata. It creates 400 hours of reasoning-augmented data by pairing erroneous hypotheses with metadata and LLM-generated explanations that justify context-driven corrections. The model is finetuned to output an initial transcript, reason over the context, and return a corrected transcript. This yields lower error rates on held-out YouTube-derived test sets, with gains concentrated on rare words and named entities. A sympathetic reader would care because existing keyword-list biasing methods do not scale to deeper knowledge while this approach uses descriptive priors to guide grounded fixes.

Core claim

Finetuning a speech-LLM on metadata-driven reasoning chains, where LLM explanations justify corrections to initial hypotheses based on video descriptions, enables the model to generate corrected transcripts after explicit reasoning and reduces recognition errors on rare words and named entities.

What carries the argument

Metadata-driven reasoning chains that link erroneous speech hypotheses to LLM-generated justifications for context-based corrections using broad video descriptions.

Load-bearing premise

The LLM-generated reasoning explanations accurately justify valid context-driven corrections that the speech-LLM can learn to ground in the audio rather than hallucinate.

What would settle it

An ablation test on the same held-out YouTube sets where the model is trained without the reasoning step and checked to see if gains on rare words and named entities disappear.

Figures

Figures reproduced from arXiv: 2606.10838 by Hugo Van hamme, Jakob Poncelet.

Figure 1
Figure 1. Figure 1: Two-stage approach for speech-LLMs that can leverage broad descriptive information to perform deep contextual reasoning. dio evidence [16, 17]. Finally, coupled decoding integrates pretrained LLM decoders during recognition of ASR models [18, 19]. Our work is complementary: rather than focusing on scaling keyword bias lists or purely text-based post-editing, we focus on training a SpeechLLM to transform br… view at source ↗
Figure 2
Figure 2. Figure 2: Example of a generated reasoning chain from a ref￾erence transcript, an ASR hypothesis and context (omitted) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
read the original abstract

Speech recognition often fails on rare, domain-specific terms and context-related named entities. Existing contextualization techniques typically bias decoding with keywords or phrase lists, which does not scale well or exploit deeper knowledge. We propose a training method that teaches a speech-LLM to use broad descriptions (e.g. from videos) as weak semantic priors to perform contextual reasoning grounded in the audio. We build 400 hours of reasoning-augmented speech data by pairing erroneous hypotheses with video metadata and LLM-generated reasoning explanations that justify context-driven corrections. We finetune the speech-LLM to perform chain-of-thought reasoning: generate an initial transcript, then reason over the context, and finally return a corrected transcript. On held-out YouTube-derived test sets, our approach reduces errors, with specific improvements on rare words and named entities, and lays groundwork for deeper contextual reasoning in speech recognition.

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

1 major / 2 minor

Summary. The paper claims to introduce a method for training speech-LLMs to perform deep contextual reasoning in ASR using broad descriptions from video metadata as weak semantic priors. They construct a 400-hour dataset by pairing erroneous ASR hypotheses with video metadata and LLM-generated reasoning explanations that justify corrections. The speech-LLM is finetuned to generate an initial transcript, perform chain-of-thought reasoning over the context, and output a corrected transcript. On held-out YouTube-derived test sets, the approach is reported to reduce errors, with particular gains on rare words and named entities.

Significance. If the central claim holds and the reasoning chains prove audio-grounded, this could advance contextual ASR beyond keyword biasing toward deeper semantic reasoning from broad descriptions. The construction of reasoning-augmented data is a concrete contribution worth noting. However, without reported quantitative results, baselines, or validation of the generated explanations, the practical significance remains difficult to assess.

major comments (1)
  1. [Dataset construction] Dataset construction (as described in the abstract and method): No human validation, inter-annotator agreement, or automatic faithfulness metric is described for the LLM-generated reasoning explanations. This is load-bearing because if a non-trivial fraction of chains contain audio-inconsistent justifications, the finetuned model could learn to echo metadata priors rather than perform audio-grounded corrections, undermining attribution of gains on rare words and named entities to the intended mechanism.
minor comments (2)
  1. The abstract and results description lack specific quantitative improvements (e.g., WER reductions), baseline comparisons, error bars, or dataset split details, which are needed to evaluate the claims.
  2. Notation for the chain-of-thought stages (initial transcript, reasoning, corrected transcript) should be formalized with equations or pseudocode for reproducibility.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for their feedback. We address the major comment below.

read point-by-point responses

  1. Referee: [Dataset construction] Dataset construction (as described in the abstract and method): No human validation, inter-annotator agreement, or automatic faithfulness metric is described for the LLM-generated reasoning explanations. This is load-bearing because if a non-trivial fraction of chains contain audio-inconsistent justifications, the finetuned model could learn to echo metadata priors rather than perform audio-grounded corrections, undermining attribution of gains on rare words and named entities to the intended mechanism.

    Authors: We agree this is a substantive concern. The submitted manuscript does not describe human validation, inter-annotator agreement, or any automatic faithfulness metric for the LLM-generated reasoning explanations. In the revised version we will add an automatic faithfulness metric (LLM-as-judge consistency check between chain, audio hypothesis, metadata, and reference) and report the pass rate on the training set. This directly addresses the risk of the model learning to echo priors. Human validation and inter-annotator agreement remain infeasible at the 400-hour scale. revision: partial

standing simulated objections not resolved
  • Human validation or inter-annotator agreement on the full set of reasoning explanations is not feasible due to dataset scale and resource limits.

Circularity Check

0 steps flagged

No circularity: empirical training pipeline is self-contained

full rationale

The paper presents an empirical method for constructing a 400-hour dataset via LLM-generated reasoning chains from metadata and erroneous hypotheses, followed by finetuning a speech-LLM for chain-of-thought correction and evaluation on held-out YouTube-derived sets. No equations, fitted parameters, or mathematical derivations are described. The central claim rests on reported error reductions rather than any reduction of a prediction to its own inputs by construction. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the provided text. This is a standard data-construction-plus-finetuning pipeline whose validity can be assessed externally via the held-out test sets.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unverified quality of LLM-generated reasoning explanations and the assumption that broad video metadata supplies usable semantic priors for audio-grounded corrections.

axioms (1)
  • domain assumption LLM-generated reasoning explanations accurately capture context-driven corrections from video metadata
    Data construction step relies on this to create the 400-hour training set.

pith-pipeline@v0.9.1-grok · 5680 in / 1242 out tokens · 19439 ms · 2026-06-27T11:33:21.156696+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

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    Therefore, research is shifting to- wards adaptation of pre-trained large language models (LLM) for speech recognition and understanding

    Introduction Current automatic speech recognition (ASR) systems can accu- rately transcribe in many languages [1, 2, 3], but remain brit- tle in situations with rare and domain-specific terminology that is acoustically ambiguous. Therefore, research is shifting to- wards adaptation of pre-trained large language models (LLM) for speech recognition and unde...

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    Towards Deep Contextual Reasoning from Broad Descriptions for ASR with Speech-LLM via Metadata-Driven Reasoning Chains

    Related Work Contextualization for ASR.Contextual ASR typically biases decoding toward a phrase list (entities, catalog items), with im- proved objectives and scalable integration [4]. In SpeechLLMs, prompt-based bias lists and fusion mechanisms can degrade as lists grow, resulting in hallucinations [6]. With retrieval tech- niques, a small candidate set ...

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    Method First, we generate reasoning chains from textual data with a text-based LLM, explaining how errors in a transcript can be solved by reasoning about the given context description. Sec- ond, we finetune a speech-LLM to perform chain-of-thought error-correcting ASR with these reasoning chains. 3.1. Reasoning Chain Generation with Video Metadata This s...

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    Experimental Results 4.1. Reasoning with Qwen2-Audio We finetune the Qwen2-Audio-7B-Instruct speech-LLM on the three dataset splits and evaluate on the diverse and challenging M³A V test set. We report several baselines: 1) the non-finetuned model, 2) finetuning with plain ASR/transcribe task without context, 3) finetuning with contextualized transcribe t...

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    Discussion The context descriptions used in this work are rather broad com- pared to time-aligned keywords or slides. Hence, we should not expect massive WER improvements, especially when segments are rather short. Still, we show that chain-of-thought reason- ing is possible (by only adapting the LLM) and that reasoning- guided training from such priors i...

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    We also introduced a pipeline and dataset that pair metadata with contextual transcript errors and correction rationales

    Conclusion We proposed a chain-of-thought ASR finetuning method that teaches a speech-LLM to use broad video descriptions for deep contextualized reasoning while staying grounded in the audio. We also introduced a pipeline and dataset that pair metadata with contextual transcript errors and correction rationales. Ex- periments show improved recognition ov...

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    Acknowledgments This research was supported by the Research Foundation Flan- ders (FWO) under grant S004923N of the SBO programme and by the Flemish Government under the ”Flanders AI Research Program”. Part of the resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (F...

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    Use of Generative AI Disclosure The authors used Generative AI exclusively for text formatting, editing, and polishing to improve the clarity of this manuscript. No part of the manuscript’s content or ideas was produced by generative AI. All authors take full responsibility and account- ability for the original work and content of this paper

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