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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 417 - 433 (17page)
- DOI
- 10.5143/JESK.2023.42.5.417
이용수
초록· 키워드
오류제보하기
Objective: The previous study explored the use of multimodality for accurate emotion predictions. However, limited research has addressed real-time implementation due to the challenges of simultaneous emotion recognition. To tackle this issue, we propose a real-time multimodal emotion recognition system based on multithreaded weighted average fusion.
Background: Emotion recognition stands as a crucial component in human-machine interaction. Challenges arise in emotion recognition due to the diverse expressions of emotions across various forms such as visual cues, auditory signals, text, and physiological responses. Recent advances in the field highlight that combining multimodal inputs, such as voice, speech, and EEG signals, yields superior results compared to unimodal approaches.
Method: We have constructed a multithreaded system to facilitate the simultaneous utilization of diverse modalities, ensuring continuous synchronization. Building upon previous work, we have enhanced our approach by incorporating weighted average fusion alongside the multithreaded system. This enhancement allows us to predict emotions based on the highest probability score.
Results: Our implementation demonstrated the ability of the proposed model to recognize and predict user emotions in real-time, resulting in improved accuracy in emotion recognition.
Conclusion: This technology has the potential to enrich user experiences and applications by enabling real-time understanding and response to human emotions.
Application: The proposed real-time multimodal emotion recognition system holds promising applications in various domains, including human-computer interaction, healthcare, and entertainment.
Background: Emotion recognition stands as a crucial component in human-machine interaction. Challenges arise in emotion recognition due to the diverse expressions of emotions across various forms such as visual cues, auditory signals, text, and physiological responses. Recent advances in the field highlight that combining multimodal inputs, such as voice, speech, and EEG signals, yields superior results compared to unimodal approaches.
Method: We have constructed a multithreaded system to facilitate the simultaneous utilization of diverse modalities, ensuring continuous synchronization. Building upon previous work, we have enhanced our approach by incorporating weighted average fusion alongside the multithreaded system. This enhancement allows us to predict emotions based on the highest probability score.
Results: Our implementation demonstrated the ability of the proposed model to recognize and predict user emotions in real-time, resulting in improved accuracy in emotion recognition.
Conclusion: This technology has the potential to enrich user experiences and applications by enabling real-time understanding and response to human emotions.
Application: The proposed real-time multimodal emotion recognition system holds promising applications in various domains, including human-computer interaction, healthcare, and entertainment.
목차
1. Introduction
2. Related Works
3. Proposed Method
4. Results and Discussion
5. Conclusion
References
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