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Acoustic Gender and Age Classification as an Aid to Human–Computer Interaction in a Smart Home Environment

Author

Listed:
  • Damjan Vlaj

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

  • Andrej Zgank

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

Abstract

The advanced smart home environment presents an important trend for the future of human wellbeing. One of the prerequisites for applying its rich functionality is the ability to differentiate between various user categories, such as gender, age, speakers, etc. We propose a model for an efficient acoustic gender and age classification system for human–computer interaction in a smart home. The objective was to improve acoustic classification without using high-complexity feature extraction. This was realized with pitch as an additional feature, combined with additional acoustic modeling approaches. In the first step, the classification is based on Gaussian mixture models. In the second step, two new procedures are introduced for gender and age classification. The first is based on the count of the frames with the speaker’s pitch values, and the second is based on the sum of the frames with pitch values belonging to a certain speaker. Since both procedures are based on pitch values, we have proposed a new, effective algorithm for pitch value calculation. In order to improve gender and age classification, we also incorporated speech segmentation with the proposed voice activity detection algorithm. We also propose a procedure that enables the quick adaptation of the classification algorithm to frequent smart home users. The proposed classification model with pitch values has improved the results in comparison with the baseline system.

Suggested Citation

  • Damjan Vlaj & Andrej Zgank, 2022. "Acoustic Gender and Age Classification as an Aid to Human–Computer Interaction in a Smart Home Environment," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:169-:d:1018733
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    References listed on IDEAS

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    1. Ja Hyung Koo & Se Woon Cho & Na Rae Baek & Young Won Lee & Kang Ryoung Park, 2022. "A Survey on Face and Body Based Human Recognition Robust to Image Blurring and Low Illumination," Mathematics, MDPI, vol. 10(9), pages 1-15, May.
    2. Ravil I. Mukhamediev & Yelena Popova & Yan Kuchin & Elena Zaitseva & Almas Kalimoldayev & Adilkhan Symagulov & Vitaly Levashenko & Farida Abdoldina & Viktors Gopejenko & Kirill Yakunin & Elena Muhamed, 2022. "Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges," Mathematics, MDPI, vol. 10(15), pages 1-25, July.
    3. Sergei Astapov & Aleksei Gusev & Marina Volkova & Aleksei Logunov & Valeriia Zaluskaia & Vlada Kapranova & Elena Timofeeva & Elena Evseeva & Vladimir Kabarov & Yuri Matveev, 2021. "Application of Fusion of Various Spontaneous Speech Analytics Methods for Improving Far-Field Neural-Based Diarization," Mathematics, MDPI, vol. 9(23), pages 1-21, November.
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