University of Oulu

Hamdi S., Moussaoui A., Oussalah M., Saidi M. (2021) Gender Identification from Arabic Speech Using Machine Learning. In: Chikhi S., Amine A., Chaoui A., Saidouni D., Kholladi M. (eds) Modelling and Implementation of Complex Systems. MISC 2020. Lecture Notes in Networks and Systems, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-58861-8_11

Gender identification from arabic speech using machine learning

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Author: Hamdi, Skander1; Moussaoui, Abdelouahab1; Oussalah, Mourad2;
Organizations: 1Department of Computer Science, University of Ferhat Abbes Setif I, Setif, Algeria
2Department of Computer Science and Engineering, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022030422007
Language: English
Published: Springer Nature, 2021
Publish Date: 2022-03-04
Description:

Abstract

Speech recognition is becoming increasingly used in real-world applications. One of the interesting applications is automatic gender recognition which aims to recognize male and female voices from short speech samples. This can be useful in applications such as automatic dialogue systems, system verification, prediction of demographic attributes (e.g., age, location) and estimating person’s emotional state. This paper focuses on gender identification from the publicly available dataset Arabic Natural Audio Dataset (ANAD) using an ensemble-classifier based approach. More specifically, initially we extended the original ANAD to include a gender label information through a manual annotation task. Next, in order to optimize the feature engineering process, a three stage machine learning approach is devised. In the first phase, re restricted to features to the two widely used ones; namely, MFCC and fundamental frequency coefficients. In the second phase, six distinct acoustic features were employed. Finally, in the third phase, the features were selected according to their associated weights in Random Forest Classifier, and the best features are thereby selected. The latter approach enabled us to achieve a classification rate of 96.02% on the test set generated with linear SVM classifier.

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Series: Lecture notes in networks and systems
ISSN: 2367-3370
ISSN-E: 2367-3389
ISSN-L: 2367-3370
ISBN: 978-3-030-58861-8
ISBN Print: 978-3-030-58860-1
Volume: 156
Pages: 149 - 162
DOI: 10.1007/978-3-030-58861-8_11
OADOI: https://oadoi.org/10.1007/978-3-030-58861-8_11
Host publication: Modelling and Implementation of Complex Systems. MISC 2020 : Lecture Notes in Networks and Systems
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. This is a post-peer-review, pre-copyedit version of an article published in Modelling and Implementation of Complex Systems. MISC 2020. Lecture Notes in Networks and Systems, vol 156. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-58861-8_11