Multiple feature fusion for unimodal emotion recognition

A new semi-serial fusion method of multiple feature based on learning using privileged information (LUPI) model was put forward. The exploitation of LUPI paradigm permits the improvement of the learning accuracy and its stability, by additional information and computations using optimization methods. The execution time is also reduced, by sparsity and dimension of testing feature. The essence of improvements obtained using multiple features types for the emotion recognition (speech expression recognition), is particularly applicable when there is only one modality but still need to improve the recognition. The results show that the LUPI in unimodal case is effective when the size of the feature is considerable. In comparison to other methods using one type of features or combining them in a concatenated way, this new method outperforms others in recognition accuracy, execution reduction, and stability.