Feature and decision level audio-visual data fusion in emotion recognition problem

The speech-based emotion recognition problem has already been investigated by many authors, and reasonable results have been achieved. This article focuses on applying audio-visual data fusion approach to emotion recognition. Two state-of-the-art classification algorithms were applied to one audio and three visual feature datasets. Feature level data fusion was applied to build a multimodal emotion classification system, which helped increase emotion classification accuracy by 4% compared to the best accuracy achieved by unimodal systems. The class precisions achieved by applying algorithms on unimodal and multimodal datasets helped to reveal that different data-classifier combinations are good at recognizing certain emotions. These data-classifier combinations were fused on the decision level using several approaches, which still helped increase the accuracy by 3% compared to the best accuracy achieved by feature level fusion.