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Voice Gender Recognition Using Acoustic Features, MFCCs and SVM

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Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13375))

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Abstract

This paper presents a voice gender recognition system. Acoustic features and Mel-Frequency Cepstral Coefficients (MFCCs) are extracted to define the speaker's gender. The most used features in these kinds of studies are acoustic features, but in this work, we combined them with MFCCs to test if we will get more satisfactory results. To examine the performance of the proposed system we tried four different databases: the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Saarbruecken Voice Database (SVD), the CMU_ARCTIC database and the Amazigh speech database (Self-Created). At the pre-processing stage, we removed the silence from the signals by using Zero-Crossing Rate (ZCR), but we kept the noises. Support Vector Machine (SVM) is used as the classification model. The combination of acoustic features and MFCCs achieves an average accuracy of 90.61% with the RAVDESS database, 92.73% with the SVD database, 99.87% with the CMU_ARCTIC database and 99.95% with the Amazigh speech database.

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Acknowledgment

A special thanks to Mr. T. Hobson from the Anglosphere English Center for reviewing for spelling and grammatical mistakes. In addition, to all participants who recorded their voices for this research.

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Authors and Affiliations

  1. Research Team of Applied Mathematics and Intelligent Systems Engineering, National School of Applied Sciences, Ibn Zohr University, 80000, Agadir, Morocco

    Fadwa Abakarim & Abdenbi Abenaou

Authors
  1. Fadwa Abakarim
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  2. Abdenbi Abenaou
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Corresponding author

Correspondence to Fadwa Abakarim .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Abakarim, F., Abenaou, A. (2022). Voice Gender Recognition Using Acoustic Features, MFCCs and SVM. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13375. Springer, Cham. https://doi.org/10.1007/978-3-031-10522-7_43

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  • DOI: https://doi.org/10.1007/978-3-031-10522-7_43

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  • Online ISBN: 978-3-031-10522-7

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