Multimodal Emotion Recognition Harnessing the Complementarity of Speech, Language, and Vision
Authors: 
Thomas Thebaud, Anna Favaro, Yaohan Guan, Yuchen Yang, Prabhav Singh, Jesus Villalba, Laureano Mono-Velazquez, Najim DehakAuthors Info & Claims
Thomas Thebaud, Anna Favaro, Yaohan Guan, Yuchen Yang, Prabhav Singh, Jesus Villalba, Laureano Mono-Velazquez, Najim DehakAuthors Info & ClaimsPages 684 - 689
Abstract
In the realm of audiovisual emotion recognition, a significant challenge lies in developing neural network architectures capable of effectively harnessing and integrating multimodal information. This study introduces an advanced methodology for the Empathic Virtual Agent Challenge (EVAC), utilizing state-of-the-art speech, language, and image models. Specifically, we leverage cutting-edge pre-trained models, including multilingual variants fine-tuned in French for each modality, and integrate them using late fusion techniques. Through extensive experimentation and validation, we demonstrate the efficacy of our approach in achieving competitive results on the challenge dataset. Our findings highlight that multimodal approaches outperform unimodal methods across Core Affect Presence and Intensity and Appraisal Dimensions tasks, underscoring the effectiveness of integrating diverse modalities. This underscores the importance of leveraging multiple sources of information to capture nuanced emotional states more accurately and robustly in real-world applications.
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Index Terms
- Multimodal Emotion Recognition Harnessing the Complementarity of Speech, Language, and Vision
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November 2024
725 pages
ISBN:9798400704628
DOI:10.1145/3678957
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
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Published: 04 November 2024
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ICMI '24
ICMI '24: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION
November 4 - 8, 2024
San Jose, Costa Rica
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