---
title: "CONVERSE: Annotation Scheme and Dataset — Torubarova 2026"
description: "Engagement-aware conversational agents need reliable ground truth, but the field has no agreed definition of engagement or a standard way to annotate it."
authors: ["Ekaterina Torubarova", "Oskar Ljung", "Julia Uddén", "André Pereira"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.218/"
doi: "10.63317/2ceqaut47as6"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/torubarova-2026-converse-annotation-scheme-dataset-multimodal/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/torubarova-2026-converse-annotation-scheme-dataset-multimodal/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/5/) / CONVERSE: Annotation Scheme and Dataset for Multimodal Conversational Engagement Analysis in Human-Human and Human-Robot Interaction

# CONVERSE: Annotation Scheme and Dataset for Multimodal Conversational Engagement Analysis in Human-Human and Human-Robot Interaction

**Authors:** Ekaterina Torubarova, Oskar Ljung, Julia Uddén, André Pereira · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.218/) · [PDF](https://aclanthology.org/2026.lrec-1.218.pdf) · [DOI 10.63317/2ceqaut47as6](https://doi.org/10.63317/2ceqaut47as6) · [OpenAlex](https://openalex.org/W7160073587)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Engagement-aware conversational agents need reliable ground truth, but the field has no agreed definition of engagement or a standard way to annotate it. The authors narrow the concept to *conversational* engagement, following Pellet-Rostaing et al. (2023). They propose a new annotation scheme that rates engagement turn by turn. They apply it to CONVERSE, a set of labels covering about 25 hours of free Swedish dialogue. In this dialogue, participants lying in an fMRI scanner talked either with a human confederate or with a Furhat robot that the same confederate teleoperated. Cameras could not capture participants' faces in the scanner, so annotators worked only from audio that was noisy from scanner sound, relying on the details of speech. The labels extend the authors' earlier NeuroEngage corpus, which already contains fMRI, eye-tracking, speech, personality and stance data. The resource is unusual because it annotates human–human and human–robot conversations with one scheme inside a single study, which allows direct comparison. It also links behavioural engagement labels to brain-imaging data, connecting multimodal machine learning, HRI and cognitive neuroscience.

## Key points
- The annotated data covers 48 native Swedish speakers: 28 in the HHI condition (about 14 h) and 20 in the HRI condition (about 9.5 h). Two of the 50 recruited participants were dropped because of poor audio.
- Each participant had three unscripted 10-minute talks about ethical dilemmas, drawn from a pool of six topics such as robot judges and an anti-aging pill.
- The confederate deliberately varied her engagement through backchannels, reactivity and proactivity. This yielded three levels of engagement, each tied to a persona: low (Passive Listener), medium (Active Listener) and high (Engaged Communicator).
- The Furhat robot was teleoperated through a VR headset that mirrored the confederate's face, gaze and head movements. The confederate used her own voice in both conditions.
- The underlying fMRI corpus (NeuroEngage, Torubarova et al., 2025) is available on OpenNeuro as ds004996. The paper contributes the scheme, the open annotation dataset and an initial analysis of the labels.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{torubarova2026converse,
  title = {{CONVERSE: Annotation Scheme and Dataset for Multimodal Conversational Engagement Analysis in Human-Human and Human-Robot Interaction}},
  author = {Torubarova, Ekaterina and Ljung, Oskar and Uddén, Julia and Pereira, André},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2ceqaut47as6},
  url = {https://doi.org/10.63317/2ceqaut47as6}
}
```

- [← previous Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources](https://md.research.elamur.ai/free/en/papers/papucci-2026-controllable-sentence-simplification-italian-fine/)
- [next → Coordination of unmatched clause types and dynamic look-ahead](https://md.research.elamur.ai/free/en/papers/lasersohn-2026-coordination-unmatched-clause-types-dynamic/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/5/)

## References

33 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Samer Al Moubayed, Jonas Beskow, Gabriel Skantze, and Björn Granström. 2012. Furhat: a back-projected human-like robot head for multiparty human-machine interaction. In Cognitive behavioural systems: COST 2102 international training school, dresden, Germany, february 21-26, 2011, revised selected pa… [find](https://scholar.google.com/scholar?q=Samer+Al+Moubayed%2C+Jonas+Beskow%2C+Gabriel+Skantze%2C+and+Bj%C3%B6rn+Granstr%C3%B6m%2E+2012%2E+Furhat%3A+a+back%2Dprojected+human%2Dlike+robot+head+for+multiparty+human%2Dmachine+interaction%2E+In+Cognitive+behavioural+systems%3A+)
2. Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim. 2017. Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions. In Proceedings of the 19th ACM international conference on multimodal interaction, pages 464Ű472. [find](https://scholar.google.com/scholar?q=Atef+Ben%2DYoussef%2C+Chlo%C3%A9+Clavel%2C+Slim+Essid%2C+Miriam+Bilac%2C+Marine+Chamoux%2C+and+Angelica+Lim%2E+2017%2E+Ue%2Dhri%3A+a+new+dataset+for+the+study+of+user+engagement+in+spontaneous+human%2Drobot+interactions%2E+In+Pro)
3. Atef Ben-Youssef, Giovanna Varni, Slim Essid, and Chloé Clavel. 2019. On-the-Ćy detection of user engagement decrease in spontaneous humanŰ robot interaction using recurrent and deep neural networks. International Journal of Social Robotics, 11(5):815Ű828. [find](https://scholar.google.com/scholar?q=Atef+Ben%2DYoussef%2C+Giovanna+Varni%2C+Slim+Essid%2C+and+Chlo%C3%A9+Clavel%2E+2019%2E+On%2Dthe%2D%C4%86y+detection+of+user+engagement+decrease+in+spontaneous+human%C5%B0+robot+interaction+using+recurrent+and+deep+neural+networks%2E+)
4. Daniele Borghesi, Andrea Amelio Ravelli, and Felice DellŠOrletta. 2022. What makes the audience engaged? engagement prediction exploiting multimodal features. In NL4AI@ AI* IA, pages 85Ű 107. [find](https://scholar.google.com/scholar?q=Daniele+Borghesi%2C+Andrea+Amelio+Ravelli%2C+and+Felice+Dell%C5%A0Orletta%2E+2022%2E+What+makes+the+audience+engaged%3F+engagement+prediction+exploiting+multimodal+features%2E+In+NL4AI%40+AI%2A+IA%2C+pages+85%C5%B0+107%2E)
5. Angelo Cafaro, Johannes Wagner, Tobias Baur, Soumia Dermouche, Mercedes Torres Torres, Catherine Pelachaud, Elisabeth André, and Michel Valstar. 2017. The noxi database: multimodal recordings of mediated novice-expert interactions. In Proceedings of the 19th ACM International Conference on Multimoda… [find](https://scholar.google.com/scholar?q=Angelo+Cafaro%2C+Johannes+Wagner%2C+Tobias+Baur%2C+Soumia+Dermouche%2C+Mercedes+Torres+Torres%2C+Catherine+Pelachaud%2C+Elisabeth+Andr%C3%A9%2C+and+Michel+Valstar%2E+2017%2E+The+noxi+database%3A+multimodal+recordings+of+media)
6. Oya Celiktutan, Efstratios Skordos, and Hatice Gunes. 2017. Multimodal human-human-robot interactions (mhhri) dataset for studying personality and engagement. IEEE Transactions on Affective Computing, 10(4):484Ű497. [find](https://scholar.google.com/scholar?q=Oya+Celiktutan%2C+Efstratios+Skordos%2C+and+Hatice+Gunes%2E+2017%2E+Multimodal+human%2Dhuman%2Drobot+interactions+%28mhhri%29+dataset+for+studying+personality+and+engagement%2E+IEEE+Transactions+on+Affective+Computing%2C)
7. Soumia Dermouche and Catherine Pelachaud. 2018. From analysis to modeling of engagement as sequences of multimodal behaviors. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). [find](https://scholar.google.com/scholar?q=Soumia+Dermouche+and+Catherine+Pelachaud%2E+2018%2E+From+analysis+to+modeling+of+engagement+as+sequences+of+multimodal+behaviors%2E+In+Proceedings+of+the+Eleventh+International+Conference+on+Language+Resour)
8. Yuyun Huang, Emer Gilmartin, and Nick Campbell. 2016. Conversational engagement recognition using auditory and visual cues. In Interspeech, pages 590Ű594. [find](https://scholar.google.com/scholar?q=Yuyun+Huang%2C+Emer+Gilmartin%2C+and+Nick+Campbell%2E+2016%2E+Conversational+engagement+recognition+using+auditory+and+visual+cues%2E+In+Interspeech%2C+pages+590%C5%B0594%2E)
9. Ryo Ishii, Yukiko I Nakano, and Toyoaki Nishida. 2013. Gaze awareness in conversational agents: Estimating a userŠs conversational engagement from eye gaze. ACM Transactions on Interactive Intelligent Systems (TiiS), 3(2):1Ű25. [find](https://scholar.google.com/scholar?q=Ryo+Ishii%2C+Yukiko+I+Nakano%2C+and+Toyoaki+Nishida%2E+2013%2E+Gaze+awareness+in+conversational+agents%3A+Estimating+a+user%C5%A0s+conversational+engagement+from+eye+gaze%2E+ACM+Transactions+on+Interactive+Intelligent)
10. Ege Kesim, Tugce Numanoglu, Oyku Bayramoglu, Bekir Berker Turker, Nusrah Hussain, Metin Sezgin, Yucel Yemez, and Engin Erzin. 2023. The ehri database: a multimodal database of engagement in humanŰrobot interactions. Language Resources and Evaluation, 57(3):985Ű1009. [find](https://scholar.google.com/scholar?q=Ege+Kesim%2C+Tugce+Numanoglu%2C+Oyku+Bayramoglu%2C+Bekir+Berker+Turker%2C+Nusrah+Hussain%2C+Metin+Sezgin%2C+Yucel+Yemez%2C+and+Engin+Erzin%2E+2023%2E+The+ehri+database%3A+a+multimodal+database+of+engagement+in+human%C5%B0robo)
11. Göran Kjellmer. 2009. Where do we backchannel?: On the use of mm, mhm, uh huh and such like. International Journal of Corpus Linguistics, 14(1):81Ű112. [find](https://scholar.google.com/scholar?q=G%C3%B6ran+Kjellmer%2E+2009%2E+Where+do+we+backchannel%3F%3A+On+the+use+of+mm%2C+mhm%2C+uh+huh+and+such+like%2E+International+Journal+of+Corpus+Linguistics%2C+14%281%29%3A81%C5%B0112%2E)
12. Dong Won Lee, Yubin Kim, Denison Guvenoz, Sooyeon Jeong, Parker Malachowsky, Louis-Philippe Morency, Cynthia Breazeal, and Hae Won Park. 2025. The human robot social interaction (hsri) dataset: Benchmarking foundational modelsŠ social reasoning. arXiv preprint arXiv:2504.13898. — [arXiv:2504.13898](https://arxiv.org/abs/2504.13898) [find](https://scholar.google.com/scholar?q=Dong+Won+Lee%2C+Yubin+Kim%2C+Denison+Guvenoz%2C+Sooyeon+Jeong%2C+Parker+Malachowsky%2C+Louis%2DPhilippe+Morency%2C+Cynthia+Breazeal%2C+and+Hae+Won+Park%2E+2025%2E+The+human+robot+social+interaction+%28hsri%29+dataset%3A+Benchm)
13. Mart Lubbers and Francisco Torreira. 2018. pympi-ling: a python module for processing elans eaf and praats textgrid annotation Ąles. Computer software]. [https://pypi.python](https://pypi.python). org/pypi/pympi-ling. [↗](https://pypi.python)
14. Gary McKeown, Michel Valstar, Roddy Cowie, Maja Pantic, and Marc Schroder. 2011. The semaine database: Annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE transactions on affective computing, 3(1):5Ű17. [find](https://scholar.google.com/scholar?q=Gary+McKeown%2C+Michel+Valstar%2C+Roddy+Cowie%2C+Maja+Pantic%2C+and+Marc+Schroder%2E+2011%2E+The+semaine+database%3A+Annotated+multimodal+records+of+emotionally+colored+conversations+between+a+person+and+a+limited+)
15. Catharine Oertel, Ginevra Castellano, Mohamed Chetouani, Jauwairia Nasir, Mohammad Obaid, Catherine Pelachaud, and Christopher Peters. 2020. Engagement in human-agent interaction: An overview. Frontiers in Robotics and AI, 7:92. [find](https://scholar.google.com/scholar?q=Catharine+Oertel%2C+Ginevra+Castellano%2C+Mohamed+Chetouani%2C+Jauwairia+Nasir%2C+Mohammad+Obaid%2C+Catherine+Pelachaud%2C+and+Christopher+Peters%2E+2020%2E+Engagement+in+human%2Dagent+interaction%3A+An+overview%2E+Frontie)
16. Arthur Pellet-Rostaing, Roxane Bertrand, Auriane Boudin, Stéphane Rauzy, and Philippe Blache. 2023. A multimodal approach for modeling engagement in conversation. Frontiers in Computer Science, 5:1062342. [find](https://scholar.google.com/scholar?q=Arthur+Pellet%2DRostaing%2C+Roxane+Bertrand%2C+Auriane+Boudin%2C+St%C3%A9phane+Rauzy%2C+and+Philippe+Blache%2E+2023%2E+A+multimodal+approach+for+modeling+engagement+in+conversation%2E+Frontiers+in+Computer+Science%2C+5%3A1062)
17. Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2018. Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508. [find](https://scholar.google.com/scholar?q=Soujanya+Poria%2C+Devamanyu+Hazarika%2C+Navonil+Majumder%2C+Gautam+Naik%2C+Erik+Cambria%2C+and+Rada+Mihalcea%2E+2018%2E+Meld%3A+A+multimodal+multi%2Dparty+dataset+for+emotion+recognition+in+conversations%2E+arXiv+preprin) [our card](https://md.research.elamur.ai/free/en/papers/poria-2018-meld-multimodal-multi-party-dataset/)
18. Carol A Prutting and Diane M Kittchner. 1987. A clinical appraisal of the pragmatic aspects of language. Journal of Speech and hearing Disorders, 52(2):105Ű119. [find](https://scholar.google.com/scholar?q=Carol+A+Prutting+and+Diane+M+Kittchner%2E+1987%2E+A+clinical+appraisal+of+the+pragmatic+aspects+of+language%2E+Journal+of+Speech+and+hearing+Disorders%2C+52%282%29%3A105%C5%B0119%2E)
19. Birgit Rauchbauer, Youssef Hmamouche, Brigitte Bigi, Laurent Prévot, Magalie Ochs, and Thierry Chaminade. 2020. Multimodal corpus of bidirectional conversation of human-human and human-robot interaction during fmri scanning. In Proceedings of the Twelfth Language Resources and Evaluation Conference,… [find](https://scholar.google.com/scholar?q=Birgit+Rauchbauer%2C+Youssef+Hmamouche%2C+Brigitte+Bigi%2C+Laurent+Pr%C3%A9vot%2C+Magalie+Ochs%2C+and+Thierry+Chaminade%2E+2020%2E+Multimodal+corpus+of+bidirectional+conversation+of+human%2Dhuman+and+human%2Drobot+interacti)
20. Justine Reverdy, Sam OŠConnor Russell, Louise Duquenne, Diego Garaialde, Benjamin R Cowan, and Naomi Harte. 2022. Roomreader: A multimodal corpus of online multiparty conversational interactions. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2517Ű2527. [find](https://scholar.google.com/scholar?q=Justine+Reverdy%2C+Sam+O%C5%A0Connor+Russell%2C+Louise+Duquenne%2C+Diego+Garaialde%2C+Benjamin+R+Cowan%2C+and+Naomi+Harte%2E+2022%2E+Roomreader%3A+A+multimodal+corpus+of+online+multiparty+conversational+interactions%2E+In+P)
21. Fabien Ringeval, Andreas Sonderegger, Juergen Sauer, and Denis Lalanne. 2013. Introducing the recola multimodal corpus of remote collaborative and affective interactions. In 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG), pages 1Ű8. IEEE. [find](https://scholar.google.com/scholar?q=Fabien+Ringeval%2C+Andreas+Sonderegger%2C+Juergen+Sauer%2C+and+Denis+Lalanne%2E+2013%2E+Introducing+the+recola+multimodal+corpus+of+remote+collaborative+and+affective+interactions%2E+In+2013+10th+IEEE+internation)
22. Harvey Sacks, Emanuel A Schegloff, and Gail Jefferson. 1974. A simplest systematics for the organization of turn-taking for conversation. language, 50(4):696Ű735. [find](https://scholar.google.com/scholar?q=Harvey+Sacks%2C+Emanuel+A+Schegloff%2C+and+Gail+Jefferson%2E+1974%2E+A+simplest+systematics+for+the+organization+of+turn%2Dtaking+for+conversation%2E+language%2C+50%284%29%3A696%C5%B0735%2E)
23. Pritam Sarkar and Ali Etemad. 2020. Self-supervised ecg representation learning for emotion recognition. IEEE Transactions on Affective Computing, 13(3):1541Ű1554. [find](https://scholar.google.com/scholar?q=Pritam+Sarkar+and+Ali+Etemad%2E+2020%2E+Self%2Dsupervised+ecg+representation+learning+for+emotion+recognition%2E+IEEE+Transactions+on+Affective+Computing%2C+13%283%29%3A1541%C5%B01554%2E)
24. Emanuel A Schegloff. 1982. Discourse as an interactional achievement: Some uses of Śuh huhŠand other things that come between sentences. Analyzing discourse: Text and talk, 71(93). [find](https://scholar.google.com/scholar?q=Emanuel+A+Schegloff%2E+1982%2E+Discourse+as+an+interactional+achievement%3A+Some+uses+of+%C5%9Auh+huh%C5%A0and+other+things+that+come+between+sentences%2E+Analyzing+discourse%3A+Text+and+talk%2C+71%2893%29%2E)
25. Emanuel A Schegloff. 2007. Sequence organization in interaction: A primer in conversation analysis I, volume 1. Cambridge university press. [find](https://scholar.google.com/scholar?q=Emanuel+A+Schegloff%2E+2007%2E+Sequence+organization+in+interaction%3A+A+primer+in+conversation+analysis+I%2C+volume+1%2E+Cambridge+university+press%2E)
26. Candace L Sidner, Christopher Lee, Cory D Kidd, Neal Lesh, and Charles Rich. 2005. Explorations in engagement for humans and robots. ArtiĄcial Intelligence, 166(1-2):140Ű164. [find](https://scholar.google.com/scholar?q=Candace+L+Sidner%2C+Christopher+Lee%2C+Cory+D+Kidd%2C+Neal+Lesh%2C+and+Charles+Rich%2E+2005%2E+Explorations+in+engagement+for+humans+and+robots%2E+Arti%C4%84cial+Intelligence%2C+166%281%2D2%29%3A140%C5%B0164%2E)
27. Silero Team. 2024. Silero vad: pre-trained enterprise-grade voice activity detector (vad), number detector and language classiĄer. [https://github.com/snakers4/silero-vad](https://github.com/snakers4/silero-vad). [↗](https://github.com/snakers4/silero-vad)
28. Alakhsimar Singh, Nischay Verma, Kanav Goyal, Amritpal Singh, Puneet Kumar, and Xiaobai Li. 2024. Visiophysioenet: Multimodal engagement detection using visual and physiological signals. arXiv preprint arXiv:2409.16126. — [arXiv:2409.16126](https://arxiv.org/abs/2409.16126) [find](https://scholar.google.com/scholar?q=Alakhsimar+Singh%2C+Nischay+Verma%2C+Kanav+Goyal%2C+Amritpal+Singh%2C+Puneet+Kumar%2C+and+Xiaobai+Li%2E+2024%2E+Visiophysioenet%3A+Multimodal+engagement+detection+using+visual+and+physiological+signals%2E+arXiv+preprin)
29. Alessandra Sorrentino, Laura Fiorini, and Filippo Cavallo. 2024. From the deĄnition to the automatic assessment of engagement in humanŰ robot interaction: A systematic review. International Journal of Social Robotics, 16(7):1641Ű 1663. [find](https://scholar.google.com/scholar?q=Alessandra+Sorrentino%2C+Laura+Fiorini%2C+and+Filippo+Cavallo%2E+2024%2E+From+the+de%C4%84nition+to+the+automatic+assessment+of+engagement+in+human%C5%B0+robot+interaction%3A+A+systematic+review%2E+International+Journal+of)
30. Kalin Stefanov and Jonas Beskow. 2016. A multi-party multi-modal dataset for focus of visual attention in human-human and human-robot interaction. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LRECŠ16), pages 4440Ű4444. [find](https://scholar.google.com/scholar?q=Kalin+Stefanov+and+Jonas+Beskow%2E+2016%2E+A+multi%2Dparty+multi%2Dmodal+dataset+for+focus+of+visual+attention+in+human%2Dhuman+and+human%2Drobot+interaction%2E+In+Proceedings+of+the+Tenth+International+Conference+)
31. Mingfei Sun, Zhenjie Zhao, and Xiaojuan Ma. 2017. Sensing and handling engagement dynamics in human-robot interaction involving peripheral computing devices. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pages 556Ű567. [find](https://scholar.google.com/scholar?q=Mingfei+Sun%2C+Zhenjie+Zhao%2C+and+Xiaojuan+Ma%2E+2017%2E+Sensing+and+handling+engagement+dynamics+in+human%2Drobot+interaction+involving+peripheral+computing+devices%2E+In+Proceedings+of+the+2017+CHI+Conference+)
32. Ekaterina Torubarova, Caroline Arvidsson, Jonathan Berrebi, Julia Uddén, and Andre Pereira. 2025. Neuroengage: A multimodal dataset integrating fmri for analyzing conversational engagement in human-human and human-robot interactions. In 2025 20th ACM/IEEE International Conference on Human-Robot Inte… [find](https://scholar.google.com/scholar?q=Ekaterina+Torubarova%2C+Caroline+Arvidsson%2C+Jonathan+Berrebi%2C+Julia+Udd%C3%A9n%2C+and+Andre+Pereira%2E+2025%2E+Neuroengage%3A+A+multimodal+dataset+integrating+fmri+for+analyzing+conversational+engagement+in+human%2Dhu)
33. Peter Wittenburg, Hennie Brugman, Albert Russel, Alex Klassmann, and Han Sloetjes. 2006. Elan: A professional framework for multimodality research. In 5th international conference on language resources and evaluation (LREC 2006), pages 1556Ű1559. [find](https://scholar.google.com/scholar?q=Peter+Wittenburg%2C+Hennie+Brugman%2C+Albert+Russel%2C+Alex+Klassmann%2C+and+Han+Sloetjes%2E+2006%2E+Elan%3A+A+professional+framework+for+multimodality+research%2E+In+5th+international+conference+on+language+resource) [our card](https://md.research.elamur.ai/free/en/papers/wittenburg-2006-elan-professional-framework-multimodality-research/)