Improving The Generalizability Of Emotion Recognition Systems: Towards Emotion Recognition In The Wild

Emotion recognition in the wild requires the ability to adapt to complex and changeable application scenarios, which necessitates the generalizability of automatic emotion recognition systems. My PhD thesis focuses on methods to address factors that negatively impact the generalizability of automatic emotion recognition systems, such as the ambiguity in emotion labels, the effects of expression style (e.g., speech and music), variation in recording environments, and individual differences. In particular, I propose to tease apart the influence of these factors from emotion using multitask learning for both feature learning and emotion inference. Results from my completed works have demonstrated that classifiers that take the influence of corpus (simulating environmental differences), expression style and gender of speaker into consideration generalize better across corpus, compared to those that do not.