Emotion Detection from Speech to Enrich Multimedia Content
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Convex Optimization
Speech Emotion Recognition Using Canonical Correlation Analysis and Probabilistic Neural Network
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
AVEC 2011-the first international audio/visual emotion challenge
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Image and Vision Computing
Shape-based modeling of the fundamental frequency contour for emotion detection in speech
Computer Speech and Language
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This paper describes a speech emotion recognition system that is built for Audio Sub-Challenge of Audio/Visual Emotion Challenge (AVEC 2011). In this system, feature selection is conducted via L1 regularized linear regression in which the L1 norm of regression weights is minimized to find a sparse weight vector. The features with approximately zero weights are removed to create a well-selected small feature set. A fusion scheme by combining the strength from linear regression and Extreme learning machine (EML) based feedforward neural networks (NN) is proposed for classification. The experiment results conducted on the SEMAINE database of naturalistic dialogues distributed through AVEC 2011 are presented.