Discrete and continuous emotion recognition using sequence kernels. (2017) - Trabelsi, Imen
The field of automatic speech emotion recognition is a highly active and multi-diverse research area. The current state-of-the-art approach in machine analysis of human emotion has focused on recognition of discrete emotional states, such as the six basic emotion categories. However, emotion is deemed complex and is characterised in terms of latent dimensions. Accordingly, this paper aims at recognising discrete and continuous emotional states by adapting the emotional recognition system to the advanced kernel-based machine learning algorithms from the field of speaker recognition, we argue that it is more efficient in terms of recognition performance. The focus in this paper is to build a range of sequence kernel to recognise discrete and continuous emotions from the well-established real-life speech dataset (IEMOCAP) and the acted Berlin emotional speech dataset (Emo-DB).