Skip to content
AI News HubLIVE
Original source2 min read

Incongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models

Summary

The paper introduces a threshold-free incongruence score measuring disagreement between the textual and acoustic emotion channels in speech, and tests whether that disagreement degrades emotion recognition in audio-capable large language models across three corpora.

SourcearXiv AIAuthor: Fengji Jing, Guy Laban
Incongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models
Report an error

The correction channel is not available yet. You can copy the article reference below for later.

Correction instructions
Read article

[Submitted on 29 Oct 2026]

Title:Incongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models

View a PDF of the paper titled Incongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models, by Fengji Jing and Guy Laban

View PDF HTML (experimental)

Abstract:Voice-interactive large language models process both what a speaker says and how they say it, yet the two channels do not always agree. We ask whether that disagreement degrades speech emotion recognition. Mapping text and audio into a shared valence-arousal-dominance space, we convert the distance between them into a continuous, threshold-free incongruence score with a two-component Gaussian mixture, and validate the score against human judgement. We evaluate three audiocapable models on acted, conversational and spontaneous speech (eNTERFACE'05, MELD and CMU-MOSEI). With the model held constant across modalities, incongruence raises the odds of error whenever audio is present, in every corpus, after controlling for the ground-truth emotion and for distance in the feature space. Supervised classifiers trained on the same features behave in the opposite way, which suggests that the difficulty lies in how these models combine raw evidence rather than in the signal itself. Text-only effects are unreliable: swapping only the model that reads the transcript reverses their sign on one corpus. Rebuilding the score without the emotion lexicon reproduces the central result, and among six simpler alternatives it is the only label-free score that predicts error in all three corpora. Code, data tables and prompts are released.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)

Cite as: arXiv:2610.40883 [cs.AI]

(or arXiv:2610.40883v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.40883

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Guy Laban [view email] [v1] Thu, 29 Oct 2026 19:04:50 UTC (434 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Incongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models, by Fengji Jing and Guy Laban

View PDF

HTML (experimental)

TeX Source

view license

Additional Features

Audio Summary

Current browse context:

cs.AI

new | recent | 2026-10

Change to browse by:

cs cs.CL cs.MM cs.SD eess eess.AS

References & Citations

SciX

Google Scholar

Semantic Scholar

Loading...

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

Key points and analysis

Article intelligence

InvestorsAdvanced

Key points

  • Text and audio are mapped into a shared valence-arousal-dominance space, and their distance is converted into a continuous, threshold-free incongruence score via a two-component Gaussian mixture, validated against human judgement.
  • Across eNTERFACE'05, MELD and CMU-MOSEI, incongruence raises the odds of error whenever audio is present, after controlling for ground-truth emotion and feature-space distance.
  • Supervised classifiers trained on the same features behave oppositely, suggesting the difficulty lies in how these models combine raw evidence rather than in the signal itself.
  • Rebuilding the score without the emotion lexicon reproduces the central result, and among six simpler alternatives it is the only label-free score predicting error in all three corpora.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

More recent stories on this topic · Models