Prediction-based learning for continuous emotion recognition in speech

Abstract : In this paper, a prediction-based learning framework is proposed for a continuous prediction task of emotion recognition from speech, which is one of the key components of affective computing in multimedia. The main goal of this framework is to utmost exploit the individual advantages of different regression models cooperatively. To this end, we take two widely used regression models for example , i.e., support vector regression and bidirectional long short-term memory recurrent neural network. We concatenate the two models in a tandem structure by different ways, forming a united cascaded framework. The outputs predicted by the former model are combined together with the original features as the input of the following model for final predictions. The experimental results on a time-and value-continuous spontaneous emotion database (RECOLA) show that, the prediction-based learning framework significantly outperforms the individual models for both arousal and valence dimensions , and provides significantly better results in comparison to other state-of-the-art methodologies on this corpus.
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Proceedings of the 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017, New Orleans (LA), United States. 2017
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Jing Han, Zixing Zhang, Fabien Ringeval, Björn Schuller. Prediction-based learning for continuous emotion recognition in speech. Proceedings of the 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017, New Orleans (LA), United States. 2017. 〈hal-01494055〉

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