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arXiv Computation and Language By Fengji Jing, Guy Laban

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

Read the original on arXiv Computation and Language →

The paper introduces an incongruence score that quantifies disagreement between a speaker’s verbal content and vocal expression in a shared valence‑arousal‑dominance space, validated against human judgments. Using this metric, the authors evaluate three audio‑capable large language models across acted, conversational, and spontaneous speech datasets, finding that higher incongruence consistently increases error rates when audio is present. The study also shows that supervised classifiers behave oppositely, suggesting the challenge lies in how models fuse multimodal evidence rather than in the signal itself, and that the score remains predictive even without an emotion lexicon.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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