Jermsittiparsert, Kittisak and Abdurrahman, Abdurrahman and Siriattakul, Parinya and Sundeeva, Ludmila A. and Hashim, Wahidah and Rahim, Robbi and Maseleno, Andino (2020) Pattern recognition and features selection for speech emotion recognition model using deep learning. International Journal of Speech Technology, 23 (4).

abe 12 2020 OK.pdf

Download (1MB) | Preview
Official URL:


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.

Item Type: Article
Subjects: L Education > L Education (General)
Divisions: Fakultas Keguruan dan Ilmu Pendidikan (FKIP) > Prodi Pendidikan Fisika
Depositing User: Dr. Abdurrahman Abdurrahman
Date Deposited: 31 May 2021 09:49
Last Modified: 31 May 2021 09:49

Actions (login required)

View Item View Item