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Improving Speaker Gender Detection by Combining Pitch and SDC

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Data Science and Applications (ICDSA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 818))

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Abstract

Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods.

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Authors and Affiliations

  1. CHRIST (Deemed to be University), Bangalore, Karnataka, India

    Aniruddha Mohanty & Ravindranath C. Cherukuri

Authors
  1. Aniruddha Mohanty
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  2. Ravindranath C. Cherukuri
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Corresponding author

Correspondence to Aniruddha Mohanty .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Satyasai Jagannath Nanda

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajendra Prasad Yadav

  3. Department of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

  4. Department of Computer Science and Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mohanty, A., Cherukuri, R.C. (2024). Improving Speaker Gender Detection by Combining Pitch and SDC. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 818. Springer, Singapore. https://doi.org/10.1007/978-981-99-7862-5_34

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