Emotion recognition in Arabic speech

Klaylat, Samira; Hamandi, Lama; Osman, Ziad; Zantout, Rached

doi:10.1109/senset.2017.8125028

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Cited by 5 publications

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“…Minimum, maximum, median, mean, and standard deviation have been calculated for each value of the f0 trajectory, and 21 MFCCs were used as features. In addition, Klaylat et al [7] achieved a 85% classification rate by Random Forest classifier in 2016 using the three benchmark labels of man, woman, and child.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of representative methods for few-shot speech gender analysis

Li,

Pan,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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As one of the basic tasks of the famous and important speech emotion recognition system, gender recognition based on speech analysis has attracted a lot of research interests in recent year. This paper considers the model performance of different machine learning methods on gender recognition in few-shot learning. In this article, we use three types of methods, namely Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Random forest. Among them, in the experiments of the SVM method, we report the linear SVM and nonlinear SVM models separately. Different experiments were performed with respect to the number of features and number of iterations and the results and possible issues were evaluated.

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Section: Introductionmentioning

confidence: 99%