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Affective Computing for Social Companion Robots Using Fine-grained Speech Emotion Recognition
Abstract
The increasing demand and diverse applications for social companion robots necessitate the development of more engaging and meaningful human-robot interactions and hence affective computing or emotion Al. In this paper, we propose a fine-grained speech emotion recognition using a state-of-the-art Deep Convolutional Neural Network trained on three-channel representations of speech signals to classify each emotion and also their intensity level. Experimental results on a publicly available dataset with intensity level (RAVEDESS) show that our method can effectively predict the users emotion and their intensity with 95.85±1.38% accuracy, a promising results towards empowering companion robots to be more affective and potentially be helpful in emotion regulations of their users.
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“Current AI systems can detect emotional cues and respond empathetically [1, 54, 61, 64].”
From Emotional Coregulation in Close Relationships with AI Agents: A Survey of ChatGPT and Replika Users (2025) · cites this paper as [1]
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Research works printed but not linked: 15
- 10.1121/1.400476 · DOI
- 10.1007/BF02943243 · DOI
- 10.1371/journal.pone.0196391 · DOI
- 10.1109/PlatCon.2017.7883728 · DOI · Paper page
- 10.1002/9781119279860.ch2 · DOI
- 10.1007/s10044-020-00921-5 · DOI
- 10.1561/1100000005 · DOI · Paper page
- 10.1007/978-3-031-19650-8_8 · DOI
- 10.1007/978-3-642-39262-7_64 · DOI
- lawrence, 2020, Improved Deep Neural Network with Age Augmentation for Facial Emotion Recognition in Social Companion Robotics, Journal of Comp Vision and Imaging Sys
- 10.1109/AEECA49918.2020.9213500 · DOI
- 10.1007/11573548_125 · DOI
- 10.5220/0010717500003061 · DOI
- anjum, 2021, Efficient Data Augmentation within Deep Learning Framework to Improve Cross-Dataset Facial Emotion Recognition, 25th International Conference on Image Processing Computer Vision Pattern Recognition
- 10.1016/j.procs.2020.08.027 · DOI
Cited by
Sorted by citations
Emotional Coregulation in Close Relationships with AI Agents: A Survey of ChatGPT and Replika Users
Pruss, E., Alimardani, M., Struijs, S., & Koole, S. L. (2025) · Proceedings of the 13th International Conference on Human-Agent Interaction · doi:10.1145/3765766.3765801 · Cited by 4
Lu, C.-K., Lu, C.-W., & Bo Lin, G. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3621086 · Cited by 4
Metadata
- Authors
- Ahuja, S., & Shabani, A.
- Affiliations
- University of the Fraser Valley (CA)
- Venue
- 2023 IEEE Conference on Artificial Intelligence (CAI)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 331-332
- OpenAlex
- W4385489149
- Cited by
- 12
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5
- FWCI
- 4.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Artificial Intelligence (CAI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- affective computing, speech emotion recognition, fine-grained emotion classification, emotion intensity estimation, human-robot interaction, deep convolutional neural networks
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