Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition

被引：0

作者：

Mathulaprangsan, Seksan ^{[1
]}

Lee, Yuan-Shan ^{[1
]}

Wang, Jia-Ching ^{[1
,2
]}

机构：

[1] Natl Cent Univ, Taoyuan, Taiwan

[2] PAIR Labs, Taipei, Taiwan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2019年 / E102D卷 / 04期

关键词：

NMF; joint dictionary learning; locality preserving; speech emotion recognition; information extraction; DICTIONARY;

D O I：

10.1587/transinf.2018DAL0002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：821 / 825

页数：5

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