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Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition
Seksan MATHULAPRANGSAN Yuan-Shan LEE Jia-Ching WANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/04/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section LETTER (Special Section on Data Engineering and Information Management)
NMF, joint dictionary learning, locality preserving, speech emotion recognition, information extraction,
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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.