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Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition

Seksan MATHULAPRANGSAN, Yuan-Shan LEE, Jia-Ching WANG

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Summary :

This study presents a joint dictionary learning approach for speech emotion recognition named locality preserved joint nonnegative matrix factorization (LP-JNMF). The learned representations are shared between the learned dictionaries and annotation matrix. Moreover, a locality penalty term is incorporated into the objective function. Thus, the system's discriminability is further improved.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.4 pp.821-825
Publication Date
2019/04/01
Publicized
2019/01/28
Online ISSN
1745-1361
DOI
10.1587/transinf.2018DAL0002
Type of Manuscript
Special Section LETTER (Special Section on Data Engineering and Information Management)
Category

Authors

Seksan MATHULAPRANGSAN
  National Central University
Yuan-Shan LEE
  National Central University
Jia-Ching WANG
  National Central University,Pervasive Artificial Intelligence Research (PAIR) Labs

Keyword