The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cette étude présente une approche d'apprentissage de dictionnaire conjoint pour la reconnaissance des émotions vocales appelée factorisation matricielle non négative conjointe à localité préservée (LP-JNMF). Les représentations apprises sont partagées entre les dictionnaires appris et la matrice d'annotation. De plus, un terme de pénalité de localité est incorporé dans la fonction objectif. Ainsi, la discriminabilité du système est encore améliorée.
Seksan MATHULAPRANGSAN
National Central University
Yuan-Shan LEE
National Central University
Jia-Ching WANG
National Central University,Pervasive Artificial Intelligence Research (PAIR) Labs
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Seksan MATHULAPRANGSAN, Yuan-Shan LEE, Jia-Ching WANG, "Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 4, pp. 821-825, April 2019, doi: 10.1587/transinf.2018DAL0002.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018DAL0002/_p
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@ARTICLE{e102-d_4_821,
author={Seksan MATHULAPRANGSAN, Yuan-Shan LEE, Jia-Ching WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition},
year={2019},
volume={E102-D},
number={4},
pages={821-825},
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.},
keywords={},
doi={10.1587/transinf.2018DAL0002},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - Locality Preserved Joint Nonnegative Matrix Factorization for Speech Emotion Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 821
EP - 825
AU - Seksan MATHULAPRANGSAN
AU - Yuan-Shan LEE
AU - Jia-Ching WANG
PY - 2019
DO - 10.1587/transinf.2018DAL0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2019
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.
ER -