Speaker-Independent Speech Emotion Recognition Based Multiple Kernel Learning of Collaborative Representation

Cheng ZHA  Xinrang ZHANG  Li ZHAO  Ruiyu LIANG  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E99-A   No.3   pp.756-759
Publication Date: 2016/03/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E99.A.756
Type of Manuscript: LETTER
Category: Engineering Acoustics
Keyword: 
multiple kernel learning,  multi-level features,  automatic segmentation,  collaborative representation,  

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Summary: 
We propose a novel multiple kernel learning (MKL) method using a collaborative representation constraint, called CR-MKL, for fusing the emotion information from multi-level features. To this end, the similarity and distinctiveness of multi-level features are learned in the kernels-induced space using the weighting distance measure. Our method achieves better performance than existing methods by using the voiced-level and unvoiced-level features.