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http://dx.doi.org/10.22937/IJCSNS.2021.21.8.46

Emotion Recognition of Low Resource (Sindhi) Language Using Machine Learning  

Ahmed, Tanveer (Mehran University of Engineering & Technology)
Memon, Sajjad Ali (Mehran University of Engineering & Technology)
Hussain, Saqib (Mehran University of Engineering & Technology)
Tanwani, Amer (Ghulam Ishaq Khan Institute of Engineering Science & Technology)
Sadat, Ahmed (Mehran University of Engineering & Technology)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.8, 2021 , pp. 369-376 More about this Journal
Abstract
One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.
Keywords
Emotion Recognition; Signal Processing; Affective Computing; Machine Learning; Dataset;
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