NDLI: Double sparse learning model for speech emotion recognition

Content Provider	IET Digital Library
Author	Zong, Yuan Zheng, Wenming Cui, Zhen Li, Qiang
Abstract	A double sparse learning (DSL) model with a pyramid structure-based feature extraction scheme to handle speech emotion recognition (SER) problem is proposed. The key novelty of the method is that the proposed DSL model is able to take into consideration two scales of the pyramid structure-based features for selecting the features which have great contributions to SER. Extensive experiments on eNTERFACE and AFEW emotion databases to evaluate the method are conducted. The experimental results demonstrate that, compared with some recent competitive methods, DSL with the pyramid structure-based feature extraction scheme has a more promising performance in dealing with the SER task.
Starting Page	1410
Ending Page	1412
Page Count	3
ISSN	00135194
Volume Number	52
e-ISSN	1350911X
Issue Number	Issue 16, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/52/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.1211
Journal	Electronics Letters
Publisher Date	2016-06-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	AFEW Emotion Databases Compressed Sensing Double Sparse Learning Model DSL Model Emotion Recognition ENTERFACE Feature Extraction Feature Extraction Scheme Knowledge Engineering Technique Learning in AI SER Speech Emotion Recognition Speech Processing Technique Speech Recognition Speech Recognition And Synthesis
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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