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Does Multi-dataset Combination Impact Machine Learning Performance? Emotion Recognition Use Case

In: Technological Innovations for Sustainable Development

Author

Listed:
  • Ezzahoud Hajar

    (Cadi Ayyad University UCA, Faculty of Sciences Semlalia, Computer Science Department)

  • Ameksa Mohammed

    (Cadi Ayyad University UCA, Faculty of Science Semlalia, FSSM, Laboratory of Computer Science and Smart Systems, LISI)

  • Amzil Asmaa

    (Cadi Ayyad University UCA, Faculty of Sciences Semlalia, Computer Science Department)

  • Amizmiz Habibatou-Allah

    (Cadi Ayyad University UCA, Faculty of Sciences Semlalia, Computer Science Department)

Abstract

Speech emotion recognition (SER) struggles with dataset diversity, model generalizability, and efficiency. Using a single dataset risks bias and limits applicability. This study combines four datasets (RAVDESS, TESS, SAVEE, CREMA-D) into a unified dataset to improve analysis and generalizability. An interpretable machine learning framework was developed, using data augmentation (e.g., noise injection, time stretching) and acoustic features (e.g., MFCC, ZCR, Chroma) to detect six emotions: anger, happiness, fear, sadness, neutral, disgust. Algorithms like SVM, LightGBM, Random Forests, XGBoost, and GBM were tested. SVM achieved 92% accuracy, followed by LightGBM at 91%. The study shows traditional machine learning can excel in SER, with dataset integration enhancing model performance and reliability.

Suggested Citation

  • Ezzahoud Hajar & Ameksa Mohammed & Amzil Asmaa & Amizmiz Habibatou-Allah, 2025. "Does Multi-dataset Combination Impact Machine Learning Performance? Emotion Recognition Use Case," Lecture Notes in Information Systems and Organization, in: Badr-Eddine Boudriki Semlali & Ikram Ben Abdel Ouahab & Fabio Angeletti (ed.), Technological Innovations for Sustainable Development, pages 38-49, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-06725-8_4
    DOI: 10.1007/978-3-032-06725-8_4
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