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Cross-Corpus Speech Emotion Recognition Based on Multiple Kernel Learning of Joint Sample and Feature Matching
Author
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-01
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yang, Ping. 2017. Cross-Corpus Speech Emotion Recognition Based on Multiple Kernel Learning of Joint Sample and Feature Matching. Journal of Electrical and Computer Engineering،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1175407
Modern Language Association (MLA)
Yang, Ping. Cross-Corpus Speech Emotion Recognition Based on Multiple Kernel Learning of Joint Sample and Feature Matching. Journal of Electrical and Computer Engineering No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1175407
American Medical Association (AMA)
Yang, Ping. Cross-Corpus Speech Emotion Recognition Based on Multiple Kernel Learning of Joint Sample and Feature Matching. Journal of Electrical and Computer Engineering. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1175407
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175407