Recognizing emotions in dialogues with acoustic and lexical features

Automatic emotion recognition has long been a focus of Affective Computing. We aim at improving the performance of state-of-the-art emotion recognition in dialogues using novel knowledge-inspired features and modality fusion strategies. We propose features based on disfluencies and nonverbal vocalisations (DIS-NVs), and show that they are highly predictive for recognizing emotions in spontaneous dialogues. We also propose the hierarchical fusion strategy as an alternative to current feature-level and decision-level fusion. This fusion strategy combines features from different modalities at different layers in a hierarchical structure. It is expected to overcome limitations of feature-level and decision-level fusion by including knowledge on modality differences, while preserving information of each modality.