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The 2026 ACII Dyadic Contest (DaiKon) Workshop & Challenge
Introducing a newly emphasized dimension of affective behavior critical to social interaction
Panagiotis Tzirakis, Alice Baird, Jeffrey Brooks, Emilia Parada-Cabaleiro, Lukas Stappen
Sponsored by Hume AI, the 2026 ACII Dyadic Contest (ACII-DaiKon) Workshop & Challenge is a workshop-based challenge that introduces the problem of modeling interpersonal affect and social dynamics in dyadic conversations. While conversational affect modeling has grown rapidly, most benchmarks and shared tasks still emphasize speaker-centric prediction rather than the coupled, time-evolving processes that emerge between two people (such as directional influence, emotional contagion, mutual adaptation, and the development of rapport over time). To the best of the organizers’ knowledge, no previous ACII workshop or mainstream machine learning challenge has offered a dedicated, three-track benchmark focused specifically on dyadic interpersonal dynamics at scale, spanning both temporal and cross-cultural variability.
[TBC] arXiv Proceedings [ White Paper GitHub
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Keynote Speakers
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Competition Tasks and Rules
The Influence Sub-Challenge, a directional dyadic dynamics prediction task (DaiKon Influence). In the DaiKon Influence sub-challenge, participants will be challenged with predicting how a target speaker’s affective state changes as a function of their partner’s preceding signals, explicitly modeling directional interpersonal influence over time. Given a multimodal dyadic conversation and historical context from both speakers, systems will output a continuous prediction for the target speaker (e.g. valence–arousal or a task-specific affect trace) for each time window. Participants will report the Concordance Correlation Coefficient (CCC), as well as Pearson correlation coefficient, for the target traces, computed separately for each direction (Speaker A to Speaker B and Speaker B to Speaker A) and averaged across directions.
The Turn-Taking Sub-Challenge, a conversational timing and speaker prediction task (DaiKon Turn Taking). In the DaiKon Turn Taking sub-challenge, participants will be challenged with predicting when and which participant will speak next, including backchannels and interruptions where labels are available. Systems will output (i) next-speaker identity as a classification task and (ii) time-to-next-speech as a regression task. Participants will report Macro-F1 / accuracy for next-speaker prediction, and MAE for time-to-next-speech, with event-based F1 for backchannels if provided.
The Rapport Trajectory Sub-Challenge, a time-evolving interaction quality prediction task (DaiKon Rapport). In the DaiKon Rapport sub-challenge, participants will be challenged with predicting how perceived rapport evolves over the course of the conversation, rather than predicting only a single final score. Given full dyadic context, systems will output a rapport trajectory (continuous or windowed), enabling models that capture gradual changes, or abrupt shifts. Participants will report the CCC, as well as Pearson correlation, between predicted and ground-truth rapport trajectories, averaged across all conversations.
Baselines and Team Ranking
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Data and Team Registration
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An overview of the data can be found at this Zenodo repository. To gain access, register your team by emailing competitions@hume.ai with the following information:
Team Name, Researcher Name, Affiliation, and Research Goals
Restricted Access: After registering your team, you will receive an End User License Agreement (EULA) for signature. Please note that this dataset is provided only for competition use. Requests for use of the data beyond the competition should be directed to Hume AI (hello@hume.ai).
Important Dates (AoE)
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Results Submission
For each task, participants should submit their test set results as a zip file to competitions@hume.ai, following these guidelines:
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Paper Submission
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Other Topics
For those interested in submitting research to the Daikon workshop outside of the competition, we encourage contributions covering the following topics:
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Technical Program Committee
As well as the organizing committee, submissions to ExVo 2022 will be reviewed by multidisciplinary researchers in the fields of emotion science, auditory and affective machine learning, and generative models.
Lukas Stappen, Recoro, Germany— Gil Keren, Meta, USA— Jeffrey Brooks,Hume AI, USA— Manuel Milling, Augsburg University, Germany— Maximillian Schmitt, audEERING, Germany — Emilia Parada-Cabarlero, JKU Linz, Austria — Esther Rituero-González, University Carlos III of Madrid, Spain—Zhao Ren, LS3, University of Hannover, Germany — Christopher Gagne, Hume AI, USA, and Max Planck Institute for Biological Cybernetics, Germany — Georgios Rizos, Imperial College London, UK.
Organizers
Dr. Panagiotis Tzirakis. Hume AI, New York, USA. panagiotis@hume.ai. [Main Contact] Panagiotis Tzirakis is an AI research scientist working at the intersection of multimodal deep learning, affective computing, and audio-visual representation learning. He obtained his Ph.D. from Imperial College London (iBUG) in 2021, where he contributed to scalable, end-to-end multimodal emotion recognition and real-world affect modeling. He publishes in leading journals and conferences including Information Fusion, International Journal of Computer Vision, ICASSP, INTERSPEECH, and ACM Multimedia (i10-index: 38). He has co-organized several workshops and challenges, including ACII-VB’22, ICML ExVo’22, and CVPR ABAW’25, ’26.
Dr. Alice Baird. Hume AI, New York, USA. alice@hume.ai. Alice Baird is an AI researcher specializing in computational paralinguistics and affective computing, with a focus on stress and emotional well-being. She received her PhD in 2021 from the University of Augsburg’s Chair of Embedded Intelligence for Health Care and Wellbeing. Her work on emotion understanding from speech, physiological, and multimodal signals, and has been widely published in leading venues such as INTERSPEECH, ICASSP, IEEE Intelligent Systems, and the IEEE Journal of Biomedical and Health Informatics (i10-index: 68). She has also co-organized international workshops and challenges, including the 2022 ACII Affective Vocal Bursts Workshop and Challenge.
Dr. Jeffrey Brooks. Hume AI, New York, U.S.A. jeff@hume.ai. Jeffrey Brooks is a computational emotion scientist with expertise in emotional expression, computational affective neuroscience, and emotional AI. He completed his PhD at New York University in 2021. His work on emotional expression and recognition in the face and voice has been published in leading interdisciplinary journals such as Nature Human Behaviour and Proceedings of the National Academy of Sciences (i10-index: 20).
Dr. Emilia Parada-Cabaleiro, University of Music Nuremberg, emiliaparada.cabaleiro@hfm-nuernberg.de
Emilia Parada-Cabaleiro received her PhD in 2016 from the University of Rome Tor Vergata, Italy. She is a music therapist, elementary music educator, and musicologist. Her research interest lay at the intersection between Psychology, Musicology, and Computer science, with a particular focus on affective computing. Her work on emotion and speech modeling has been widely published in leading international venues, including INTERSPEECH and other top conferences in speech and affective computing (i10-index: 33).
Dr. Lukas Stappen, BMW Group, Munich, Germany lukas.stappen@bmw.de. Lukas Stappen is an AI researcher with expertise in multimodal learning, affective computing, and large language models. He obtained his PhD from the University of Augsburg in 2021, where he contributed to human-centric multimodal understanding and also (co-)organized international workshops and challenges, including founding the MuSe Challenge series (2020-2024) for advancing multimodal sentiment analysis. His current interest focuses on LLM-based voice assistants and AI safety. His work has been widely published in leading venues, such as IEEE Transactions on Affective Computing, ACM Multimedia, ACL, INTERSPEECH, and ICASSP (i10-index: 26).