arxiv: 2601.21463 · v2 · submitted 2026-01-29 · 💻 cs.SD · cs.AI

Recognition: no theorem link

Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs

Jun Xue , Yi Chai , Yanzhen Ren , Jinshen He , Zhiqiang Tang , Zhuolin Yi , Yihuan Huang , Yuankun Xie

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Yujie Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-16 09:58 UTC · model grok-4.3

classification 💻 cs.SD cs.AI

keywords speech editing detectioncontent localizationAudio LLMsAiEdit datasetprior-enhanced promptingacoustic consistency lossgenerative formulationdeletion edits

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The pith

Audio LLMs reformulated as text generators detect speech edits and localize their content on a new realistic dataset.

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

Current speech editing detection relies on spotting frame-level acoustic anomalies and uses datasets built from manual splicing, which miss deletion edits where content is removed entirely and fail to reflect modern end-to-end editing tools. The paper introduces the AiEdit dataset of roughly 140 hours of bilingual audio that includes addition, deletion, and modification operations generated by state-of-the-art editing systems. It reframes the detection task as structured text generation inside Audio LLMs, supplies word-level probability cues from a separate frame detector through prior-enhanced prompting, and adds an acoustic consistency loss that pushes normal and anomalous latent representations apart. If the reformulation works, detection and localization become joint generative reasoning rather than separate anomaly classification steps, allowing the model to infer missing content even when no signal remains.

Core claim

We present a unified framework that bridges speech editing detection and content localization through a generative formulation based on Audio Large Language Models. We first introduce AiEdit, a large-scale bilingual dataset that covers addition, deletion, and modification operations using state-of-the-art end-to-end speech editing systems. Building upon this, we reformulate SED as a structured text generation task, enabling joint reasoning over edit type identification and content localization. To enhance the grounding of generative models in acoustic evidence, we propose a prior-enhanced prompting strategy that injects word-level probabilistic cues derived from a frame-level detector. An ac

What carries the argument

Prior-enhanced prompting that injects word-level probabilistic cues from a frame-level detector into an Audio LLM to drive structured text generation of edit type and content location.

If this is right

Deletion edits become detectable because the generative formulation reasons about absent content rather than requiring an anomalous frame to be present.
Joint text generation produces both edit-type labels and precise content boundaries in a single pass.
The acoustic consistency loss creates clearer separation between normal and manipulated representations in the LLM latent space.
Performance gains appear on both detection and localization metrics when compared with frame-level supervised baselines.

Where Pith is reading between the lines

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

The same generative reformulation could be tested on other audio manipulation problems such as music splicing or environmental sound alteration.
Real-time verification pipelines might integrate the prior cue injection step to flag edited segments before full transcription.
Because the dataset is bilingual, cross-language transfer experiments could reveal whether the prompting strategy generalizes without retraining.
Future datasets could deliberately include hybrid edits that combine multiple editing systems to probe whether the current coverage assumption holds.

Load-bearing premise

The AiEdit dataset built with current end-to-end editing systems supplies realistic examples of modern threats and the prior-enhanced prompts successfully anchor the generative model to actual acoustic evidence.

What would settle it

Run the trained model on audio edited by entirely new, previously unseen editing algorithms and measure whether detection accuracy and localization precision fall below the reported levels on AiEdit.

Figures

Figures reproduced from arXiv: 2601.21463 by Jinshen He, Jun Xue, Yanzhen Ren, Yi Chai, Yihuan Huang, Yuankun Xie, Yujie Chen, Zhiqiang Tang, Zhuolin Yi.

**Figure 1.** Figure 1: Spectrogram comparison across different editing samples. Fully Synthetic Forgery: Early research on fake speech detection primarily focused on fully synthetic attacks, which aim to spoof entire utterances to compromise automatic speaker verification (ASV) systems, as exemplified by the ASVspoof (Todisco et al., 2019; Yamagishi et al., 2021) and ADD (Yi et al., 2023b) challenge series. As illustrated in F… view at source ↗

**Figure 2.** Figure 2: Self-attention heatmaps over the audio token region in the last Transformer layer. and coherent prosody of bona fide samples, synthetic speech often sounds mechanical, with overly deliberate and complete articulation, leading to unnatural listening impressions. HumanEdit: To preserve most characteristics of the original speaker while manipulating local semantics, researchers have proposed partial forgery… view at source ↗

**Figure 4.** Figure 4: Distribution of Part-of-Speech (POS) tags for edited words. The pie charts illustrate the proportion of different syntactic categories targeted for editing in the Chinese (left) and English (right) subsets of our dataset. to specific forgery artifacts, we introduce three representative editing paradigms: (1) end-to-end reconstruction models (e.g., SSR (Wang et al., 2025a) and VoiceCraft (Peng et al., 202… view at source ↗

**Figure 5.** Figure 5: Overview of the PELM architecture, including prior-enhanced multi-modality input construction, audio LLM-based reasoning, and centroid clustering-based training objective. text tampering schemes covering diverse parts of speech, including nouns, verbs, adjectives, and conjunctions, via context-aware reasoning. Specifically, different languages exhibit distinct editing preferences: in the English subset, no… view at source ↗

**Figure 6.** Figure 6: Performance evaluation of different models on speech editing tasks, including HumanEdit, AiEdit, and Pool. The left and right panels display the accuracy results under Detection and Localization level, respectively. Detailed results are provided in Appendix C. capability to precisely identify the edited words within the utterance. The evaluation metrics include Accuracy, Area Under the Curve (AUC), F1 scor… view at source ↗

**Figure 7.** Figure 7: Prompt details of generic prompt System Prompt You are an expert in Speech Editing Deepfake Detection, specializing in detecting and temporally localizing editing-based tampering in speech content. Please analyze whether the input speech has been edited. If any speech content has been added, modified or deleted, identify the corresponding editing type and the associated words spans, otherwise, report that … view at source ↗

**Figure 8.** Figure 8: Prompt details of task definition only prompt 14 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

**Figure 9.** Figure 9: Prompt details of complete task definition with strict requirements 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

read the original abstract

Existing speech editing detection (SED) datasets are predominantly constructed using manual splicing or limited editing operations, resulting in restricted diversity and poor coverage of realistic editing scenarios. Meanwhile, current SED methods rely heavily on frame-level supervision to detect observable acoustic anomalies, which fundamentally limits their ability to handle deletion-type edits, where the manipulated content is entirely absent from the signal. To address these challenges, we present a unified framework that bridges speech editing detection and content localization through a generative formulation based on Audio Large Language Models (Audio LLMs). We first introduce AiEdit, a large-scale bilingual dataset (approximately 140 hours) that covers addition, deletion, and modification operations using state-of-the-art end-to-end speech editing systems, providing a more realistic benchmark for modern threats. Building upon this, we reformulate SED as a structured text generation task, enabling joint reasoning over edit type identification, and content localization. To enhance the grounding of generative models in acoustic evidence, we propose a prior-enhanced prompting strategy that injects word-level probabilistic cues derived from a frame-level detector. Furthermore, we introduce an acoustic consistency-aware loss that explicitly enforces the separation between normal and anomalous acoustic representations in the latent space. Experimental results demonstrate that the proposed approach consistently outperforms existing methods across both detection and localization tasks.

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's real value is the AiEdit dataset built with modern end-to-end editors and the shift to generative Audio LLMs that can reason about deletions instead of just spotting waveform glitches.

read the letter

The main thing to know is that this work creates a larger, more realistic benchmark for speech editing detection and tries to solve the deletion problem by turning the task into structured text generation with Audio LLMs. They inject word-level priors from a frame detector and add a loss that separates normal and anomalous acoustics in the latent space. That combination lets the model identify edit type and localize content even when the original material is gone from the signal, which frame-level methods miss by design. The dataset itself covers addition, deletion, and modification at scale with current editing tools, and it's bilingual, which is a practical step beyond the old manual-splice collections. The generative reformulation plus the prior and consistency pieces are a direct attempt to ground the LLM output in acoustic evidence rather than letting it freewheel. This is useful for anyone who needs both detection and localization in one pass. The soft spot is that the outperformance claim still needs the actual tables, baselines, splits, and ablations to be convincing; without those numbers visible, it's hard to judge how large the gains really are or whether the dataset splits avoid leakage. The realism of the edits also rests on how well the chosen SOTA systems match real-world threats, which may need extra validation. For readers in audio forensics or misinformation work, the dataset alone is worth pulling, and the prompting strategy is worth testing if you're already using Audio LLMs. It should go to peer review because the problem is concrete, the dataset is new, and the method has enough structure to get useful feedback even if the experiments need tightening.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces AiEdit, a large-scale bilingual dataset (~140 hours) constructed using state-of-the-art end-to-end speech editing systems to cover addition, deletion, and modification operations. It proposes a unified generative framework that reformulates speech editing detection (SED) and content localization as structured text generation with Audio LLMs, incorporating prior-enhanced prompting that injects word-level probabilistic cues from an external frame-level detector and an acoustic consistency-aware loss to separate normal and anomalous representations in latent space. The central claim is that this approach consistently outperforms existing methods on both detection and localization tasks.

Significance. If the experimental claims hold with proper validation, the work would be significant for audio forensics. The AiEdit dataset addresses the documented limitations of manual-splicing datasets by providing realistic coverage of modern editing threats, while the generative reformulation and prior-injection mechanism directly target the inability of frame-level methods to handle deletion edits. Releasing such a benchmark and demonstrating grounding of Audio LLMs in acoustic evidence could shift the field toward more generalizable, reasoning-based detectors.

major comments (3)

[§4] §4 Experiments (and associated tables): the abstract and introduction assert consistent outperformance, yet no quantitative metrics, baseline comparisons, error bars, dataset splits, or ablation results are referenced in the provided description; without these, the central claim that the prior-enhanced prompting and consistency loss are responsible for gains cannot be evaluated.
[§3.2] §3.2 Prior-Enhanced Prompting: the strategy relies on an external frame-level detector to supply word-level priors; the manuscript must demonstrate (via ablation or controlled comparison) that this injection is load-bearing rather than the performance simply inheriting from the detector, especially for deletion edits where acoustic evidence is absent.
[§2] §2 AiEdit Dataset Construction: details are required on how the SOTA end-to-end editors were configured, how edit boundaries were annotated for localization ground truth, and what steps were taken to ensure the generated edits reflect realistic acoustic artifacts rather than artifacts of the editing pipeline itself.

minor comments (2)

[§3.3] Notation for the acoustic consistency loss should be clarified with an explicit equation showing how the separation term is computed in the latent space.
[§4] The bilingual nature of AiEdit is mentioned but language-specific performance breakdowns are not referenced; adding these would strengthen the localization claims.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments highlight important areas for clarification and strengthening, particularly around experimental reporting, ablation evidence, and dataset construction details. We address each major comment below and will incorporate revisions to improve the manuscript.

read point-by-point responses

Referee: [§4] §4 Experiments (and associated tables): the abstract and introduction assert consistent outperformance, yet no quantitative metrics, baseline comparisons, error bars, dataset splits, or ablation results are referenced in the provided description; without these, the central claim that the prior-enhanced prompting and consistency loss are responsible for gains cannot be evaluated.

Authors: The full Experiments section (§4) contains the requested elements: multiple tables reporting detection (AUC, EER) and localization (F1, IoU) metrics with comparisons to frame-level baselines and prior generative methods, error bars from 5 random seeds, explicit train/validation/test splits of AiEdit, and ablation tables isolating the prior-enhanced prompting and acoustic consistency loss. The abstract and introduction summarize the outcomes without numbers to maintain brevity. We will revise the abstract and §1 to include targeted cross-references (e.g., “achieving 12.4% relative AUC improvement over the strongest baseline, see Table 3”) so readers can immediately locate the supporting evidence. revision: yes
Referee: [§3.2] §3.2 Prior-Enhanced Prompting: the strategy relies on an external frame-level detector to supply word-level priors; the manuscript must demonstrate (via ablation or controlled comparison) that this injection is load-bearing rather than the performance simply inheriting from the detector, especially for deletion edits where acoustic evidence is absent.

Authors: We agree that an explicit demonstration is necessary. The current ablation study (Table 5) already compares the full model against a no-prior variant that uses only the base Audio LLM; performance drops are largest on deletion edits (localization F1 falls by 9.7 points), confirming the priors supply critical cues when acoustic evidence is missing. We will expand this ablation with a controlled comparison that replaces the external detector priors with random or uniform word-level scores, further isolating the contribution of the detector-derived probabilities. revision: partial
Referee: [§2] §2 AiEdit Dataset Construction: details are required on how the SOTA end-to-end editors were configured, how edit boundaries were annotated for localization ground truth, and what steps were taken to ensure the generated edits reflect realistic acoustic artifacts rather than artifacts of the editing pipeline itself.

Authors: We will substantially expand §2.3–2.5 with the missing implementation details: exact model versions and inference hyperparameters for the three end-to-end editors, the two-stage annotation protocol (automatic boundary extraction followed by human verification by three annotators with inter-annotator agreement statistics), and the realism validation steps (listener ABX tests, spectro-temporal artifact comparison against manually spliced references, and acoustic-feature distribution matching). These additions will clarify that the edits preserve genuine editing artifacts rather than pipeline-specific ones. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces a new AiEdit dataset (~140 hours) built with external SOTA end-to-end editors and reformulates SED as text generation on pre-trained Audio LLMs. It adds prior-enhanced prompting (word-level cues from a separate frame-level detector) and an acoustic consistency loss. These components are presented as direct engineering responses to stated limitations of prior frame-level methods. Outperformance is shown via new experiments on the introduced dataset rather than by reducing any prediction to a fitted input or self-citation chain. No self-definitional equations, fitted-input-as-prediction, or load-bearing self-citations appear; the central claims rest on external pre-trained models plus fresh empirical validation and are therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The approach assumes Audio LLMs can be effectively prompted for audio reasoning tasks and that an external frame-level detector provides useful priors; the new dataset and loss function are introduced without independent verification details in the abstract.

axioms (1)

domain assumption Audio LLMs can perform structured text generation tasks that incorporate acoustic evidence when given appropriate priors
Invoked when reformulating SED as a text generation task with prior-enhanced prompting.

invented entities (1)

AiEdit dataset no independent evidence
purpose: Provide realistic benchmark covering addition, deletion, and modification edits
Newly constructed collection of approximately 140 hours of bilingual edited speech.

pith-pipeline@v0.9.0 · 5554 in / 1326 out tokens · 44973 ms · 2026-05-16T09:58:55.142736+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
cs.SD 2026-04 unverdicted novelty 3.0

AT-ADD introduces standardized tracks and datasets for evaluating audio deepfake detectors on speech under real-world conditions and on diverse unknown audio types to promote generalization beyond speech-centric methods.

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · cited by 1 Pith paper

[1]

Must originate from the original context

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[2]

Delete Removes a segment of text from the original sentence

Onlyonecontinuous insertion point is allowed per sample. Delete Removes a segment of text from the original sentence

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[3]

Removal of words at thestart or endof the sentence is strictly prohibited to preserve context integrity

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[4]

Modify Replaces a segment of the original text with new content

Onlyonecontinuous deletion region is allowed. Modify Replaces a segment of the original text with new content

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[5]

The replacement must maintain a similar length to the original segment

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[6]

Finally, to ensure reproducibility, the detailed hyperparameters and environmental settings used for the parallel text editing tool are listed in Table 6

Onlyonemodification region is allowed (i.e., one replacement). Finally, to ensure reproducibility, the detailed hyperparameters and environmental settings used for the parallel text editing tool are listed in Table 6. Table 6.Hyperparameters and settings for the Qwen-based parallel text editing tool. Parameter Value Description Model Name Qwen3-max The ba...

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[7]

No evidence of speech editing was detected

If the speech is completely authentic, output: "No evidence of speech editing was detected."

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[8]

If any editing (added/modified/deleted) is detected, output:

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[9]

Yes, '<exact_text>' was [Type] in speech. The [Type] MUST be one of the following: added, modified

If the editing is added or modified, output: "Yes, '<exact_text>' was [Type] in speech. The [Type] MUST be one of the following: added, modified."

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[10]

Yes, some words were deleted in speech

If the editing is deleted, output: "Yes, some words were deleted in speech."

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[11]

Treat every speech fairly

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[12]

Output ONLY the result string

Do not output any other text or explanation. Output ONLY the result string. Output Examples: No evidence of speech editing was detected. Yes, ' 不 ' was added in speech. Yes, 'dull' was modified in speech. Yes, some words were deleted in speech. User Prompt <audio>The uploaded speech is a speech recording. Please analyze whether the speech has been edited....

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