Abstract-Automatic prediction of continuous-level emotional state requires selection of suitable affective features to develop a regression system based on supervised machine learning. This paper investigates the performance of features statistically learned using convolutional neural networks for instantaneously predicting the continuous dimensions of emotional states. Features with minimum redundancy and maximum relevancy are chosen by using the mutual information-based selection process. The performance of frame-by-frame prediction of emotional state using the moderate length features as proposed in this paper is evaluated on spontaneous and naturalistic human-human conversation of RECOLA database. Experimental results show that the proposed model can be used for instantaneous prediction of emotional state with an accuracy higher than traditional audio or video features that are used for affective computation.
To obtain the automatic prediction of continuously emotion states through the appropriate features, Basnet et al. REF investigated the statistical performance of various features extracted from different convolutional neural networks.
Statistical Selection of CNN-based Audiovisual Features for Instantaneous Estimation of Human Emotional States
2017 International Conference on New Trends in Computing Sciences (ICTCS)
2017 International Conference on New Trends in Computing Sciences (ICTCS)
Computer Science