SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues?

Gyuhyeon Seo; Jonggeun Lee; Junseong Pyo; Yohan Jo

arxiv: 2601.04029 · v2 · submitted 2026-01-07 · 💻 cs.CL

SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues?

Jonggeun Lee , Junseong Pyo , Gyuhyeon Seo , Yohan Jo This is my paper

Pith reviewed 2026-05-16 16:14 UTC · model grok-4.3

classification 💻 cs.CL

keywords large audio-language modelsspeaker consistencymulti-turn dialoguesacoustic evaluationmodality biasspeech generationbenchmark

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The pith

Large audio-language models prioritize text over acoustics when judging speaker consistency in multi-turn dialogues.

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

The paper creates SpeakerSleuth, a benchmark with three tasks to assess whether large audio-language models can judge if the same speaker is consistent across dialogue turns using audio evidence. Evaluation of twelve models on human-verified instances from synthetic and real speech shows they often fail to detect acoustic inconsistencies, either overpredicting changes or being too permissive. When text from other speakers is added as context, the models' performance drops sharply because they rely on textual flow rather than listening to the audio. In contrast, the models show stronger ability when directly comparing or ranking different acoustic versions of the same content. These results point to a core imbalance where text dominates over sound in how these models make judgments about audio dialogues.

Core claim

LALMs struggle to reliably judge speaker consistency across multi-turn dialogues. Given audio samples from the same speaker, some models overpredict inconsistency while others are overly lenient. When textual context from other interlocutors is provided, performance degrades as models prioritize textual coherence over acoustic cues and fail to detect even obvious changes such as gender switches. Models perform better when comparing and ranking acoustic variants, indicating they possess acoustic discrimination abilities but do not apply them effectively in consistency evaluation tasks.

What carries the argument

The SpeakerSleuth benchmark, which consists of three tasks designed to evaluate LALMs on speaker consistency detection with varying acoustic difficulty levels across four datasets.

If this is right

LALMs cannot yet serve as reliable judges for speaker consistency in audio dialogues due to their detection struggles.
Providing textual context causes models to ignore acoustic information and focus on text.
Models have inherent acoustic discrimination capabilities as shown by better performance in comparison and ranking tasks.
Addressing the text-over-acoustics bias is necessary to create reliable audio-language judges.

Where Pith is reading between the lines

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

Similar modality biases may exist in other evaluation tasks involving audio and language.
Future model training could incorporate techniques to balance attention between text and audio modalities.
The benchmark might be adapted to assess consistency in other attributes like emotion or speaking style.
Real-world dialogue systems could benefit from hybrid judges that combine LALMs with dedicated acoustic analyzers.

Load-bearing premise

The benchmark's three tasks and controlled difficulty levels capture the essential real-world demands for speaker consistency judgment, with human verification providing accurate ground truth labels.

What would settle it

Demonstrating that LALMs correctly identify speaker inconsistencies like gender switches even when textual context is provided would challenge the finding of text prioritization bias.

read the original abstract

Large Audio-Language Models (LALMs) as judges have emerged as a prominent approach for evaluating speech generation quality, yet their ability to assess speaker consistency across multi-turn dialogues remains unexplored. We present \textbf{SpeakerSleuth}, a benchmark evaluating whether LALMs can reliably judge speaker consistency across multi-turn dialogues through three tasks reflecting real-world requirements. We construct 1,818 human-verified evaluation instances across four diverse datasets spanning synthetic and real speech, with controlled acoustic difficulty. Evaluating twelve widely-used LALMs, we find that models struggle to reliably detect acoustic inconsistencies. For instance, given audio samples of the same speaker's turns, some models overpredict inconsistency, whereas others are overly lenient. Models further struggle to identify the exact turns that are problematic. When other interlocutors' turns are provided as textual context, performance degrades dramatically as models prioritize textual coherence over acoustic cues, failing to detect even obvious gender switches for a speaker. On the other hand, models perform substantially better in comparing and ranking acoustic variants, demonstrating inherent acoustic discrimination capabilities. These findings expose a significant bias in LALMs: they tend to prioritize text over acoustics, revealing fundamental modality imbalances that need to be addressed to build reliable audio-language judges. Our code and data are available at https://github.com/holi-lab/SpeakerSleuth.

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

SpeakerSleuth gives a clean empirical picture of LALMs favoring text over acoustics when judging speaker consistency, backed by a new human-verified benchmark.

read the letter

The key point is that current LALMs struggle to judge speaker consistency in multi-turn audio dialogues and default to text cues even when acoustics clearly contradict them, such as missing gender switches once interlocutor turns are supplied as text. The paper builds SpeakerSleuth with three tasks—detecting inconsistencies, pinpointing bad turns, and ranking acoustic variants—using 1,818 human-verified instances drawn from four datasets that mix synthetic and real speech with controlled difficulty levels. They test twelve models and show consistent patterns: decent acoustic discrimination in isolation, sharp drops when text context appears, and over- or under-prediction of inconsistencies depending on the model. Code and data are public, which helps reproducibility. The results line up with the abstract and stress-test note, with no obvious internal contradictions in the task design or reported numbers. The main soft spot is that any benchmark of this kind will carry some selection effects around how inconsistencies were inserted and how acoustic difficulty was calibrated, though human verification reduces that risk. The three-task structure and the text-over-acoustics finding are new relative to the cited prior work. This paper is useful for anyone building or auditing audio-language models as judges for dialogue or speech generation systems. The empirical grounding is solid enough that it deserves a serious referee rather than a desk reject.

Referee Report

2 major / 2 minor

Summary. The paper introduces SpeakerSleuth, a benchmark with three tasks for evaluating whether Large Audio-Language Models (LALMs) can judge speaker consistency across multi-turn dialogues. It constructs 1,818 human-verified instances spanning four synthetic and real-speech datasets with controlled acoustic difficulty levels, then evaluates twelve LALMs. The central empirical finding is that models exhibit a strong text-over-acoustics bias: performance degrades sharply when textual interlocutor turns are supplied (including failure to detect obvious gender switches), while pure acoustic variant ranking is substantially stronger.

Significance. If the reported patterns hold, the work supplies direct evidence of a modality imbalance in current LALMs that limits their reliability as audio-language judges. The public release of code and data, together with the scale of the human-verified test set, makes the contribution reproducible and extensible. The results are relevant to any application that relies on LALMs for speech evaluation or dialogue assessment.

major comments (2)

[Section 4] Section 4 (Experiments) and Table 2: the dramatic degradation when textual context is added is presented as the key evidence for text-over-acoustics bias, yet no statistical significance tests (p-values, confidence intervals, or paired comparisons) are reported for the performance drops across the twelve models; without these, it is difficult to judge whether the observed differences are robust or could be explained by variance in the 1,818-instance set.
[Section 3.2] Section 3.2 (Benchmark Construction): the claim that acoustic difficulty is 'controlled' across instances is central to attributing failures to modality bias rather than acoustic complexity; the manuscript should explicitly state the acoustic features or metrics used for stratification and report how many instances fall into each difficulty bin.

minor comments (2)

[Abstract] Abstract: the exact number of models (twelve) and datasets (four) should be stated numerically rather than left implicit.
[Figure 3] Figure 3 caption: the legend for acoustic-variant ranking curves is unclear about which line corresponds to which model family.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and constructive comments. We address the two major points below and will incorporate clarifications and additional analyses in the revised manuscript.

read point-by-point responses

Referee: [Section 4] Section 4 (Experiments) and Table 2: the dramatic degradation when textual context is added is presented as the key evidence for text-over-acoustics bias, yet no statistical significance tests (p-values, confidence intervals, or paired comparisons) are reported for the performance drops across the twelve models; without these, it is difficult to judge whether the observed differences are robust or could be explained by variance in the 1,818-instance set.

Authors: We agree that statistical significance testing would strengthen the presentation of the results. In the revised manuscript we will add paired tests (McNemar's test for the consistency detection tasks and Wilcoxon signed-rank test for ranking) together with 95% confidence intervals for all reported metrics in Table 2 and the associated text. These additions will confirm that the observed performance drops are statistically significant. revision: yes
Referee: [Section 3.2] Section 3.2 (Benchmark Construction): the claim that acoustic difficulty is 'controlled' across instances is central to attributing failures to modality bias rather than acoustic complexity; the manuscript should explicitly state the acoustic features or metrics used for stratification and report how many instances fall into each difficulty bin.

Authors: We acknowledge that the current manuscript does not provide sufficient detail on the stratification procedure. We will revise Section 3.2 to explicitly describe the acoustic metrics used (signal-to-noise ratio, speaker-embedding cosine similarity from a pre-trained verification model, and prosodic variation) and will add a table reporting the number of instances per difficulty bin (low/medium/high) for each of the four source datasets. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a purely empirical benchmark study. It defines three tasks, constructs 1,818 human-verified instances across synthetic and real datasets with controlled acoustic difficulty, runs twelve LALMs, and reports direct performance metrics (detection accuracy, ranking quality, degradation under textual context). No equations, parameter fits, uniqueness theorems, or self-citations are used to derive the central claims; the text-over-acoustics bias is observed from the experimental patterns themselves. The work is self-contained against external benchmarks and human ground truth.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that human verification produces accurate ground-truth labels for speaker consistency and that the chosen tasks reflect real-world evaluation needs. No free parameters are fitted and no new entities are postulated.

axioms (1)

domain assumption Human verification provides reliable ground truth for speaker consistency labels.
The paper uses 1,818 human-verified instances as the basis for all model evaluations.

pith-pipeline@v0.9.0 · 5551 in / 1175 out tokens · 51518 ms · 2026-05-16T16:14:49.584267+00:00 · methodology

discussion (0)

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We present SpeakerSleuth, a benchmark evaluating whether LALMs can reliably judge speaker consistency across multi-turn dialogues through three tasks... 1,818 human-verified evaluation instances... models prioritize textual coherence over acoustic cues

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