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Pattern recognition and features selection for speech emotion recognition model using deep learning

Deep learning; Feature extraction; Learning systems; Speech; Feature selection methods; Features selection; Input sources; Maximum accuracies; Recognition models; Recognizing models; Speech emotion recognition; Speech sounds; Speech recognition

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Main Authors: Jermsittiparsert K., Abdurrahman A., Siriattakul P., Sundeeva L.A., Hashim W., Rahim R., Maseleno A.
Other Authors: 57214268798
Format: Article
Published: Springer 2023
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spelling my.uniten.dspace-251342023-05-29T16:06:54Z Pattern recognition and features selection for speech emotion recognition model using deep learning Jermsittiparsert K. Abdurrahman A. Siriattakul P. Sundeeva L.A. Hashim W. Rahim R. Maseleno A. 57214268798 57006623600 57209603370 57218851305 11440260100 57212431318 55354910900 Deep learning; Feature extraction; Learning systems; Speech; Feature selection methods; Features selection; Input sources; Maximum accuracies; Recognition models; Recognizing models; Speech emotion recognition; Speech sounds; Speech recognition Automatic speaker recognizing models consists of a foundation on building various models of speaker characterization, pattern analyzing and engineering. The effect of classification and feature selection methods for the speech emotion recognition is focused. The process of selecting the exact parameter in arrangement with the classifier is an important part of minimizing the difficulty of system computing. This process becomes essential particularly for the models which undergo deployment in real time scenario. In this paper, a new deep learning speech based recognition model is presented for automatically recognizes the speech words. The superiority of an input source, i.e. speech sound in this state has straight impact on a classifier correctness attaining process. The Berlin database consist around 500 demonstrations to media persons that is both male and female. On the applied dataset, the presented model achieves a maximum accuracy of 94.21%, 83.54%, 83.65% and 78.13% under MFCC, prosodic, LSP and LPC features. The presented model offered better recognition performance over the other methods. � 2020, Springer Science+Business Media, LLC, part of Springer Nature. Final 2023-05-29T08:06:54Z 2023-05-29T08:06:54Z 2020 Article 10.1007/s10772-020-09690-2 2-s2.0-85090466191 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090466191&doi=10.1007%2fs10772-020-09690-2&partnerID=40&md5=1a55fbba14ef5feef18a20851196622a https://irepository.uniten.edu.my/handle/123456789/25134 23 4 799 806 Springer Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Deep learning; Feature extraction; Learning systems; Speech; Feature selection methods; Features selection; Input sources; Maximum accuracies; Recognition models; Recognizing models; Speech emotion recognition; Speech sounds; Speech recognition
author2 57214268798
author_facet 57214268798
Jermsittiparsert K.
Abdurrahman A.
Siriattakul P.
Sundeeva L.A.
Hashim W.
Rahim R.
Maseleno A.
format Article
author Jermsittiparsert K.
Abdurrahman A.
Siriattakul P.
Sundeeva L.A.
Hashim W.
Rahim R.
Maseleno A.
spellingShingle Jermsittiparsert K.
Abdurrahman A.
Siriattakul P.
Sundeeva L.A.
Hashim W.
Rahim R.
Maseleno A.
Pattern recognition and features selection for speech emotion recognition model using deep learning
author_sort Jermsittiparsert K.
title Pattern recognition and features selection for speech emotion recognition model using deep learning
title_short Pattern recognition and features selection for speech emotion recognition model using deep learning
title_full Pattern recognition and features selection for speech emotion recognition model using deep learning
title_fullStr Pattern recognition and features selection for speech emotion recognition model using deep learning
title_full_unstemmed Pattern recognition and features selection for speech emotion recognition model using deep learning
title_sort pattern recognition and features selection for speech emotion recognition model using deep learning
publisher Springer
publishDate 2023
_version_ 1806426567601029120
score 13.209306

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