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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 8639782, 6 pages
Research Article

Cross-Corpus Speech Emotion Recognition Based on Multiple Kernel Learning of Joint Sample and Feature Matching

College of Big Data and Information Engineering, Guizhou University, Guiyang 550002, China

Correspondence should be addressed to Ping Yang; nc.ude.uzg@3gnayp

Received 5 April 2017; Revised 3 August 2017; Accepted 13 September 2017; Published 1 November 2017

Academic Editor: Ping Feng Pai

Copyright © 2017 Ping Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Cross-corpus speech emotion recognition, which learns an accurate classifier for new test data using old and labeled training data, has shown promising value in speech emotion recognition research. Most previous works have explored two learning strategies independently for cross-corpus speech emotion recognition: feature matching and sample reweighting. In this paper, we show that both strategies are important and inevitable when the distribution difference is substantially large for training and test data. We therefore put forward a novel multiple kernel learning of joint sample and feature matching (JSFM-MKL) to model them in a unified optimization problem. Experimental results demonstrate that the proposed JSFM-MKL outperforms the competitive algorithms for cross-corpus speech emotion recognition.