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

Ezzahoud Hajar (), Ameksa Mohammed (), Amzil Asmaa () and Amizmiz Habibatou-Allah ()
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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

A chapter in Technological Innovations for Sustainable Development, 2025, pp 38-49 from Springer

Abstract: 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.

Keywords: Dataset Diversity; Acoustic Features; Model Generalizability; Machine Learning Algorithms (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-06725-8_4

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