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CMTD: Cognitive Modeling with Traits and Distortions for Multimodal Emotion Recognition in Conversations

Paper · 2026 · Minh-Tien Nguyen, Huu-Loi Le, Manh-Cuong Phan et al.

Summary

CMTD tackles multimodal emotion recognition in conversations (MERC). The authors argue that current systems read emotion too superficially and miss the psychological factors behind it. Their framework is a zero-shot, prompt-based multi-agent pipeline. It pairs long-lasting personality traits (Big Five), used as a proxy for static emotional tendencies, with short-term cognitive distortions, which reflect dynamic emotional states. These psychological signals are combined with facial and vocal cues.

The pipeline has five agents:

  • a trait agent;
  • a distortion agent, which extends Diagnosis-of-Thought (DoT) by splitting it into several sub-agents instead of one long prompt;
  • a visual agent that reads a single representative frame;
  • a speech agent that verbalizes acoustic properties;
  • a fusion agent that issues the final label.

Experiments on MELD and IEMOCAP indicate that this cognitive and multimodal combination improves MERC. Traits appear to counteract the negativity bias that distortion detection alone tends to introduce. The main drawbacks are higher latency and cost than the baselines. The work shows how ideas from cognitive behavioral therapy can make LLM-based emotion reasoning more robust.

Key points

  • Distortion agent: built on DoT, it runs four stages: subjectivity assessment, contrastive reasoning, schema analysis, and distortion detection. It produces labels such as Mind Reading, Should Statements, and Magnification.
  • Speech agent: describes four acoustic features (frequency, energy, speaking rate, voice quality).
  • Visual agent: uses a middle-frame strategy, which saves cost but loses temporal facial dynamics.
  • Evaluation: zero-shot, with no parameter tuning, on MELD and IEMOCAP, using gpt-5-mini and gpt-4.1 as backbones. An ablation study is included.
  • Efficiency: the reported cost/time table ranges up to about 25× runtime (≈4167 s, ≈1.64 USD) relative to the cheapest gpt-4.1 setting. The extracted text does not make clear which row is the full CMTD.
  • Limitations and availability:
    • Both datasets are acted (TV sitcoms and scripted dialogues).
    • Agent collaboration is a simple single-judge fusion.
    • Code is released at github.com/Shaun-le/CMTD.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Hung Yen University of Technology and EducationHưng Yên · Vietnam2 works · 2 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 works

🧭 Topics 3Research subjects and languages the work is about.Sentiment & emotion · 972 worksVision & language · 657 worksPsycholinguistics & language acquisition · 102 works

Cite

Cite as Minh-Tien Nguyen, Huu-Loi Le, Manh-Cuong Phan and Hajime Hotta. 2026. CMTD: Cognitive Modeling with Traits and Distortions for Multimodal Emotion Recognition in Conversations. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.41

@inproceedings{nguyen2026cmtd,
  title = {{CMTD: Cognitive Modeling with Traits and Distortions for Multimodal Emotion Recognition in Conversations}},
  author = {Minh-Tien Nguyen, Huu-Loi Le, Manh-Cuong Phan and Hajime Hotta},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.41},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.41}
}

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