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Gender Recognition by Voice Using an Improved Self-Labeled Algorithm

1
Department of Computer & Informatics Engineering, Technological Educational Institute of Western Greece, GR 263-34 Antirion, Greece
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Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
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Mach. Learn. Knowl. Extr. 2019, 1(1), 492-503; https://doi.org/10.3390/make1010030
Received: 22 January 2019 / Revised: 19 February 2019 / Accepted: 2 March 2019 / Published: 5 March 2019
Speech recognition has various applications including human to machine interaction, sorting of telephone calls by gender categorization, video categorization with tagging and so on. Currently, machine learning is a popular trend which has been widely utilized in various fields and applications, exploiting the recent development in digital technologies and the advantage of storage capabilities from electronic media. Recently, research focuses on the combination of ensemble learning techniques with the semi-supervised learning framework aiming to build more accurate classifiers. In this paper, we focus on gender recognition by voice utilizing a new ensemble semi-supervised self-labeled algorithm. Our preliminary numerical experiments demonstrate the classification efficiency of the proposed algorithm in terms of accuracy, leading to the development of stable and robust predictive models. View Full-Text
Keywords: semi-supervised learning; self-labeled methods; ensemble learning; gender recognition; classification semi-supervised learning; self-labeled methods; ensemble learning; gender recognition; classification
MDPI and ACS Style

Livieris, I.E.; Pintelas, E.; Pintelas, P. Gender Recognition by Voice Using an Improved Self-Labeled Algorithm. Mach. Learn. Knowl. Extr. 2019, 1, 492-503.

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