Earticle

초록

영어

The main objective of this study is to investigate the impact of additional modalities on the performance of emotion recognition using speech, facial expression and physiological measurements. In order to compare different approaches, we designed a feature-based recognition system as a benchmark which carries out linear supervised classification followed by the leave-one-out cross-validation. For the classification of four emotions, it turned out that bimodal fusion in our experiment improves recognition accuracy of unimodal approach, while the performance of trimodal fusion varies strongly depending on the individual. Furthermore, we experienced extremely high disparity between single class recognition rates, while we could not observe a best performing single modality in our experiment. Based on these observations, we developed a novel fusion method, called parametric decision fusion (PDF), which lies in building emotion-specific classifiers and exploits advantage of a parametrized decision process. By using the PDF scheme we achieved 16% improvement in accuracy of subject-dependent recognition and 10% for subject-independent recognition compared to the best unimodal results.

목차

Abstract
1. Introduction
2. Related Work
3. Trimodal Dataset
3.1 Experimental Setting
3.2 Collected Sensor Data
4. General Methodology and Result
4.1 Multimodal Feature Calculation
4.2 Classification
4.3 Recognition Results
5. Parametric Decision Fusion
5.1 Building Dichotomous Classifiers
5.2 Cascaded Specialists Algorithm (CSA)
5.3 Making Decision
5.4 Results
6. Conclusion
References

키워드

저자

• Jonghwa Kim [ Professor, Dept. Intelligent System Engineering, Cheju Halla University, Jeju Island, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380