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

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

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.

原文	???core.languages.en_GB???
頁（從 - 到）	821-825
頁數	5
期刊	IEICE Transactions on Information and Systems
卷	E102D
發行號	4
DOIs	https://doi.org/10.1587/transinf.2018DAL0002
出版狀態	已出版 - 1 4月 2019

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10.1587/transinf.2018DAL0002

智慧型照護互動系統－基於深度智能之口語處理技術(2/4)
Wang, J.
1/01/19 → 31/12/19
研究計畫: Research

引用此

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title = "Locality preserved joint nonnegative matrix factorization for speech emotion recognition",

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{\textquoteright}s discriminability is further improved.",

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

author = "Seksan Mathulaprangsan and Lee, {Yuan Shan} and Wang, {Jia Ching}",

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AU - Lee, Yuan Shan

AU - Wang, Jia Ching

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N2 - 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.

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.

KW - Information extraction

KW - Joint dictionary learning

KW - Locality preserving

KW - NMF

KW - Speech emotion recognition

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Locality preserved joint nonnegative matrix factorization for speech emotion recognition

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智慧型照護互動系統－基於深度智能之口語處理技術(2/4)

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