Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition

Seksan MATHULAPRANGSAN  Yuan-Shan LEE  Jia-Ching WANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.4   pp.821-825
Publication Date: 2019/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018DAL0002
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,  

Full Text: PDF(1.1MB)>>
Buy this Article

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.