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Automatic Speech Emotion Recognition of Younger School Age Children

Author

Listed:
  • Yuri Matveev

    (Child Speech Research Group, Department of Higher Nervous Activity and Psychophysiology, St. Petersburg State University, St. Petersburg 199034, Russia)

  • Anton Matveev

    (Child Speech Research Group, Department of Higher Nervous Activity and Psychophysiology, St. Petersburg State University, St. Petersburg 199034, Russia)

  • Olga Frolova

    (Child Speech Research Group, Department of Higher Nervous Activity and Psychophysiology, St. Petersburg State University, St. Petersburg 199034, Russia)

  • Elena Lyakso

    (Child Speech Research Group, Department of Higher Nervous Activity and Psychophysiology, St. Petersburg State University, St. Petersburg 199034, Russia)

  • Nersisson Ruban

    (School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India)

Abstract

This paper introduces the extended description of a database that contains emotional speech in the Russian language of younger school age (8–12-year-old) children and describes the results of validation of the database based on classical machine learning algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). The validation is performed using standard procedures and scenarios of the validation similar to other well-known databases of children’s emotional acting speech. Performance evaluation of automatic multiclass recognition on four emotion classes “Neutral (Calm)—Joy—Sadness—Anger” shows the superiority of SVM performance and also MLP performance over the results of perceptual tests. Moreover, the results of automatic recognition on the test dataset which was used in the perceptual test are even better. These results prove that emotions in the database can be reliably recognized both by experts and automatically using classical machine learning algorithms such as SVM and MLP, which can be used as baselines for comparing emotion recognition systems based on more sophisticated modern machine learning methods and deep neural networks. The results also confirm that this database can be a valuable resource for researchers studying affective reactions in speech communication during child-computer interactions in the Russian language and can be used to develop various edutainment, health care, etc. applications.

Suggested Citation

  • Yuri Matveev & Anton Matveev & Olga Frolova & Elena Lyakso & Nersisson Ruban, 2022. "Automatic Speech Emotion Recognition of Younger School Age Children," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2373-:d:856949
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    References listed on IDEAS

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    1. Adriana-Mihaela Guran & Grigoreta-Sofia Cojocar & Laura-Silvia Dioşan, 2022. "The Next Generation of Edutainment Applications for Young Children—A Proposal," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
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    Cited by:

    1. Jagjeet Singh & Lakshmi Babu Saheer & Oliver Faust, 2023. "Speech Emotion Recognition Using Attention Model," IJERPH, MDPI, vol. 20(6), pages 1-21, March.
    2. Yoonseok Heo & Sangwoo Kang, 2023. "A Simple Framework for Scene Graph Reasoning with Semantic Understanding of Complex Sentence Structure," Mathematics, MDPI, vol. 11(17), pages 1-15, August.

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