Locality preserved joint nonnegative matrix factorization for speech emotion recognition

Seksan Mathulaprangsan; Yuan Shan Lee; Jia Ching Wang

doi:10.1587/transinf.2018DAL0002

Locality preserved joint nonnegative matrix factorization for speech emotion recognition

Seksan Mathulaprangsan, Yuan Shan Lee, Jia Ching Wang

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	821-825
Number of pages	5
Journal	IEICE Transactions on Information and Systems
Volume	E102D
Issue number	4
DOIs	https://doi.org/10.1587/transinf.2018DAL0002
State	Published - 1 Apr 2019

Keywords

Information extraction
Joint dictionary learning
Locality preserving
NMF
Speech emotion recognition

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10.1587/transinf.2018DAL0002

Deep Intelligence Based Spoken Language Processing( II )
Wang, J.-C. (PI)
1/01/19 → 31/12/19
Project: Research

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keywords = "Information extraction, Joint dictionary learning, Locality preserving, NMF, Speech emotion recognition",

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AB - 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.

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KW - Locality preserving

KW - NMF

KW - Speech emotion recognition

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Locality preserved joint nonnegative matrix factorization for speech emotion recognition

Abstract

Keywords

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Projects

Deep Intelligence Based Spoken Language Processing( II )

Cite this