arxiv: 2604.12506 · v1 · submitted 2026-04-14 · 💻 cs.CL · cs.SD

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Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs

Linhao Zhang , Yuhan Song , Aiwei Liu , Chuhan Wu , Sijun Zhang , Wei Jia , Yuan Liu , Houfeng Wang

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Xiao Zhou

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Pith reviewed 2026-05-10 14:45 UTC · model grok-4.3

classification 💻 cs.CL cs.SD

keywords audio large language modelsunified audio schemaparalinguistic perceptionacoustic event detectionstructured supervisionJSON audio formatperception-aware trainingaudio reasoning

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The pith

A three-component JSON schema for audio lets AudioLLMs perceive tone and events while keeping strong reasoning.

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

The paper argues that Audio Large Language Models perform well on reasoning but poorly on fine-grained acoustic perception because training focused on speech transcription teaches them to treat tone, emotion, and background sounds as noise to ignore. The authors introduce the Unified Audio Schema, which structures every audio clip into three explicit parts inside one JSON object: the spoken words, the paralinguistic details such as pitch and emotion, and the non-linguistic events such as door slams or music. This balanced target set supplies complete acoustic information without breaking the direct audio-to-text mapping that supports reasoning. Experiments on standard benchmarks show the resulting models improve perception scores while reasoning performance stays intact.

Core claim

Organizing audio information into the three components of Transcription, Paralinguistics, and Non-linguistic Events within a unified JSON format provides comprehensive acoustic coverage and improves fine-grained perception by 10.9 percent on MMSU over same-size state-of-the-art models without sacrificing reasoning capabilities.

What carries the argument

The Unified Audio Schema (UAS), a structured supervision framework that places transcription, paralinguistic features, and non-linguistic events into one JSON object and thereby supplies explicit targets for every acoustic aspect during training.

If this is right

Models trained with UAS show higher accuracy on tasks that require detecting tone, emotion, and background sounds on MMSU, MMAR, and MMAU.
The same schema improves both discrete-token and continuous-feature AudioLLM architectures.
Reasoning performance on complex logic and question-answering tasks remains at the same level as before the change.
The gains appear consistently across multiple benchmarks without requiring larger model size.

Where Pith is reading between the lines

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

The same three-part breakdown could be applied to video models so they learn to link visual events with matching sounds and speech.
Longer audio recordings might be handled by first segmenting them and then applying the schema to each segment before combining the JSON outputs.
Other multimodal models facing conflicting objectives could adopt similar explicit multi-aspect targets to reduce interference between perception and reasoning.

Load-bearing premise

That ASR-centric training is the main reason models suppress paralinguistic and event information, and that switching to the three-part JSON schema will not create new alignment or noise problems that cancel the gains.

What would settle it

Train an AudioLLM with the UAS schema on the same data and measure no gain or a loss in fine-grained perception scores on MMSU compared with the original ASR-trained version, or observe a clear drop in reasoning accuracy.

Figures

Figures reproduced from arXiv: 2604.12506 by Aiwei Liu, Chuhan Wu, Houfeng Wang, Linhao Zhang, Sijun Zhang, Wei Jia, Xiao Zhou, Yuan Liu, Yuhan Song.

**Figure 1.** Figure 1: Overview of the Unified Audio Schema (UAS) and evaluation results. (a) UAS structures audio information into three components: Transcription, Paralinguistics, and Non-linguistic Events. (b) Reasoning vs. Perception accuracy on MMSU. UAS-Audio significantly enhances perception while maintaining robust reasoning. information (Laver, 1994) and is designed to expose perceptual dimensions explicitly rather tha… view at source ↗

**Figure 2.** Figure 2: Overview of the UAS Data Generation Pipeline. (Stage 1) Acoustic captions are generated to capture [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of UAS-Audio architectures. Audio input is processed via either discrete tokenization or continuous encoding (left). The LLM generates textonly or bi-modal outputs (right). cific UAS fields (e.g., “What is the speaker’s emotion?” → “Neutral”; “What is the speaker’s accent?” → “Southern American English”); (2) Multiple Choice: Questions with candidate options derived from the UAS attribute voc… view at source ↗

**Figure 4.** Figure 4: shows ablation results on MMSU. Both UAS annotation and UAS-QA contribute substantially to perception: removing UAS drops accuracy by 6.3%, removing UAS-QA by 9.6%, and removing both by 15.0%. The larger impact of UAS-QA suggests that explicit question-answering training is more critical for translating acoustic knowledge into task performance. Crucially, reasoning accuracy remains stable across all conf… view at source ↗

**Figure 5.** Figure 5: The web-based human evaluation interface. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

**Figure 6.** Figure 6: Prompt for using the Qwen3-30B-A3B-Instruct model to perform Caption-to-UAS Conversion [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: Prompt for using the Qwen3-235B-A22B-Instruct model to perform QA Generation from UAS input [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

read the original abstract

Recent Audio Large Language Models (AudioLLMs) exhibit a striking performance inversion: while excelling at complex reasoning tasks, they consistently underperform on fine-grained acoustic perception. We attribute this gap to a fundamental limitation of ASR-centric training, which provides precise linguistic targets but implicitly teaches models to suppress paralinguistic cues and acoustic events as noise. To address this, we propose Unified Audio Schema (UAS), a holistic and structured supervision framework that organizes audio information into three explicit components -- Transcription, Paralinguistics, and Non-linguistic Events -- within a unified JSON format. This design achieves comprehensive acoustic coverage without sacrificing the tight audio-text alignment that enables reasoning. We validate the effectiveness of this supervision strategy by applying it to both discrete and continuous AudioLLM architectures. Extensive experiments on MMSU, MMAR, and MMAU demonstrate that UAS-Audio yields consistent improvements, boosting fine-grained perception by 10.9% on MMSU over the same-size state-of-the-art models while preserving robust reasoning capabilities. Our code and model are publicly available at https://github.com/Tencent/Unified_Audio_Schema.

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 attributes the underperformance of AudioLLMs on fine-grained acoustic perception (despite strong reasoning) to ASR-centric training that suppresses paralinguistic and event cues. It introduces Unified Audio Schema (UAS), a JSON-structured supervision with three explicit components—Transcription, Paralinguistics, and Non-linguistic Events—and applies it to both discrete and continuous AudioLLM architectures. Experiments on MMSU, MMAR, and MMAU report consistent gains, including a 10.9% lift in fine-grained perception on MMSU over same-size SOTA models, while preserving reasoning; code and models are released publicly.

Significance. If the gains are attributable to the structured schema rather than ancillary factors, UAS could provide a practical route to perception-aware AudioLLMs without sacrificing the tight audio-text alignment needed for reasoning. Public code release is a clear strength that supports reproducibility and follow-on work.

major comments (2)

[Abstract / Experiments] Abstract and experimental results: the central claim that UAS overcomes ASR-induced suppression of paralinguistic cues rests on the reported 10.9% MMSU improvement, yet no ablation is described that holds training data volume, epochs, and optimizer fixed while varying only the target structure (full three-component JSON versus transcription-only). Without this isolation, the delta could be explained by increased supervision volume or diversity rather than the specific schema organization.
[Experiments] Experimental details: the manuscript provides no description of how the Paralinguistics and Non-linguistic Events fields in the UAS JSON labels were created, annotated, or validated for quality, nor any error bars, confidence intervals, or statistical tests on the benchmark deltas. These omissions make it impossible to assess whether the gains are robust or sensitive to annotation noise.

minor comments (2)

[Abstract] The abstract states that UAS is applied to 'both discrete and continuous AudioLLM architectures' but does not specify the exact model sizes, baseline checkpoints, or training hyperparameters used in each case, which would aid direct replication.
Figure or table captions (if present) should explicitly note whether results are averaged over multiple seeds; the absence of such detail compounds the lack of error bars already noted.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to strengthen the presentation of our results.

read point-by-point responses

Referee: [Abstract / Experiments] Abstract and experimental results: the central claim that UAS overcomes ASR-induced suppression of paralinguistic cues rests on the reported 10.9% MMSU improvement, yet no ablation is described that holds training data volume, epochs, and optimizer fixed while varying only the target structure (full three-component JSON versus transcription-only). Without this isolation, the delta could be explained by increased supervision volume or diversity rather than the specific schema organization.

Authors: We agree that a controlled ablation isolating the contribution of the UAS structure itself is needed to fully support the central claim. Our reported gains are relative to same-size SOTA models whose training is predominantly transcription-centric, but we did not include an intra-experiment ablation that fixes data volume, epochs, and optimizer while varying only the supervision format. In the revised manuscript we will add this ablation, training matched model configurations on identical data with either full UAS JSON targets or transcription-only targets. This will allow direct attribution of gains to the structured schema rather than supervision volume. revision: yes
Referee: [Experiments] Experimental details: the manuscript provides no description of how the Paralinguistics and Non-linguistic Events fields in the UAS JSON labels were created, annotated, or validated for quality, nor any error bars, confidence intervals, or statistical tests on the benchmark deltas. These omissions make it impossible to assess whether the gains are robust or sensitive to annotation noise.

Authors: We acknowledge these omissions limit assessment of robustness. In the revised manuscript we will add a dedicated subsection describing the creation, annotation, and quality-validation procedures for the Paralinguistics and Non-linguistic Events fields. We will also report error bars (standard deviation across runs), confidence intervals, and statistical significance tests (e.g., paired t-tests or Wilcoxon tests) for all benchmark deltas on MMSU, MMAR, and MMAU. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical validation on public benchmarks with no derivations or self-referential fits

full rationale

The paper introduces the UAS schema as a structured supervision format and reports performance gains from applying it to existing AudioLLM architectures on MMSU, MMAR, and MMAU. No equations, parameter fits, or predictions appear in the provided text. The central claim rests on comparative results against state-of-the-art models using public datasets rather than any internal normalization, self-citation chain, or construction that equates outputs to inputs by definition. This is a standard empirical proposal whose evidence is externally falsifiable.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that audio content decomposes cleanly into the three named categories without overlap or loss, plus the empirical claim that the resulting labels improve perception without harming reasoning. No free parameters or invented physical entities are introduced.

axioms (1)

domain assumption Audio signals can be exhaustively and non-overlappingly partitioned into transcription, paralinguistics, and non-linguistic events.
Invoked in the description of the UAS framework as the basis for comprehensive coverage.

pith-pipeline@v0.9.0 · 5518 in / 1262 out tokens · 34314 ms · 2026-05-10T14:45:47.049156+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 3 canonical work pages · 1 internal anchor

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Unstructured Caption

in both settings to strictly isolate the impact of the supervision format while controlling for model architecture and training data. Target Format Perception Reasoning Average Unstructured Caption48.4 75.5 61.5 Structured UAS 54.8 76.0 65.2 Table 7: Impact of supervision format on MMSU. The "Unstructured Caption" setting serves as a proxy for caption-bas...