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

Simple item page
dc.citedby20
dc.contributor.authorJermsittiparsert K.en_US
dc.contributor.authorAbdurrahman A.en_US
dc.contributor.authorSiriattakul P.en_US
dc.contributor.authorSundeeva L.A.en_US
dc.contributor.authorHashim W.en_US
dc.contributor.authorRahim R.en_US
dc.contributor.authorMaseleno A.en_US
dc.contributor.authorid57214268798en_US
dc.contributor.authorid57006623600en_US
dc.contributor.authorid57209603370en_US
dc.contributor.authorid57218851305en_US
dc.contributor.authorid11440260100en_US
dc.contributor.authorid57212431318en_US
dc.contributor.authorid55354910900en_US
dc.date.accessioned2023-05-29T08:06:54Z
dc.date.available2023-05-29T08:06:54Z
dc.date.issued2020
dc.descriptionDeep 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 recognitionen_US
dc.description.abstractAutomatic 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s10772-020-09690-2
dc.identifier.epage806
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85090466191
dc.identifier.spage799
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090466191&doi=10.1007%2fs10772-020-09690-2&partnerID=40&md5=1a55fbba14ef5feef18a20851196622a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25134
dc.identifier.volume23
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Speech Technology
dc.titlePattern recognition and features selection for speech emotion recognition model using deep learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS