Fusion of multiple emotion perspectives: Improving affect recognition through integrating cross-lingual emotion information

Developing cross-corpus, cross-domain, and cross-language emotion recognition algorithm has becoming more prevalent recently to ensure the wide applicability of robust emotion recognizer. In this work, we propose a computational framework on fusing multiple emotion perspectives by integrating cross-lingual emotion information. By assuming that each data is `perceived' not only by a main perspective but additional derived perspectives (from a corpus of a different language), we can then combine each of the perspective-dependent features via kernel fusion technique. In specifics, we utilize two emotional corpora of different languages (Chinese and English). Our experiments demonstrate that our proposed framework achieves significant improvement over single perspective baseline across both databases.