Automated speech emotion polarization for a distance education system based on orbital local binary pattern and an appropriate sub-band selection technique

Dahiru Tanko; Fahrettin Burak Demir; Sengul Dogan; Sakir Engin Sahin; Turker Tuncer

doi:10.1007/s11042-023-14648-y

Automated speech emotion polarization for a distance education system based on orbital local binary pattern and an appropriate sub-band selection technique

Multimed Tools Appl. 2023 Apr 3:1-18. doi: 10.1007/s11042-023-14648-y. Online ahead of print.

Authors

Dahiru Tanko¹, Fahrettin Burak Demir², Sengul Dogan¹, Sakir Engin Sahin³, Turker Tuncer¹

Affiliations

¹ Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
² Deparment of Software Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Bandirma, Turkey.
³ Department of Computer Technologies, Arapgir Vocational School, Malatya Turgut Ozal University, Malatya, Turkey.

Abstract

The distance education system was widely adopted during the Covid-19 pandemic by many institutions of learning. To measure the effectiveness of this system, it is essential to evaluate the performance of the lecturers. To this end, an automated speech emotion recognition model is a solution. This research aims to develop an accurate speech emotion recognition model that will check the lecturers/instructors' emotional state during lecture presentations. A new speech emotion dataset is collected, and an automated speech emotion recognition (SER) model is proposed to achieve this aim. The presented SER model contains three main phases, which are (i) feature extraction using multi-level discrete wavelet transform (DWT) and one-dimensional orbital local binary pattern (1D-OLBP), (ii) feature selection using neighborhood component analysis (NCA), (iii) classification using support vector machine (SVM) with ten-fold cross-validation. The proposed 1D-OLBP and NCA-based model is tested on the collected dataset, containing three emotional states with 7101 sound segments. The presented 1D-OLBP and NCA-based technique achieved a 93.40% classification accuracy using the proposed model on the new dataset. Moreover, the proposed architecture has been tested on the three publicly available speech emotion recognition datasets to highlight the general classification ability of this self-organized model. We reached over 70% classification accuracies for all three public datasets, and these results demonstrated the success of this model.

Keywords: 1D-OLBP; Distance education; NCA; SVM; Speech emotion polarization; Speech emotion recognition.

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