Article: An integrated hybrid deep learning framework of speech emotion recognition for personalised movie recommendations Journal: International Journal of Artificial Intelligence and Soft Computing (IJAISC) 2025 Vol.9 No.4 pp.379 - 394 Abstract: The study investigates the intricate relationship between SER and CRS by utilising advanced machine learning. It proposes a recommendation mechanism that goes beyond the conventional outcome of a content-based system to extract specific emotional nuances from speech data. It examines the emotional subtleties and the genre of the movies by integrating movie datasets with emotional classification. It further evaluates the emotional impact of movies and provides recommendations to viewers through vectorisation, cosine similarity, and genre-specific emotional weight. Overall, the classifiers reflect the system's outstanding performance in mapping user preferences to suggested movies, indicating a major improvement in recommendation systems. With an F1 score of 93%, the proposed CNN (93%) is identified as the best integrated deep learning model among other classifiers that which includes MLP (73%), LightGBM (90%), XGBoost (91%), Naïve Bayes (34%), AdaBoost (49%), k-NN (74%), logistic regression (43%), LSTM (84%), RF (88%), DT (82%), and SVM (48%). Inderscience Publishers - linking academia, business and industry through research

Title: An integrated hybrid deep learning framework of speech emotion recognition for personalised movie recommendations

Authors: Syed Azeem Inam; Ghulam Mustafa; Abdul Rahim

Addresses: Department of Artificial Intelligence and Mathematical Sciences, Sindh Madressatul Islam University, Karachi, Pakistan ' Department of Artificial Intelligence and Mathematical Sciences, Sindh Madressatul Islam University, Karachi, Pakistan ' Department of Artificial Intelligence and Mathematical Sciences, Sindh Madressatul Islam University, Karachi, Pakistan

Abstract: The study investigates the intricate relationship between SER and CRS by utilising advanced machine learning. It proposes a recommendation mechanism that goes beyond the conventional outcome of a content-based system to extract specific emotional nuances from speech data. It examines the emotional subtleties and the genre of the movies by integrating movie datasets with emotional classification. It further evaluates the emotional impact of movies and provides recommendations to viewers through vectorisation, cosine similarity, and genre-specific emotional weight. Overall, the classifiers reflect the system's outstanding performance in mapping user preferences to suggested movies, indicating a major improvement in recommendation systems. With an F1 score of 93%, the proposed CNN (93%) is identified as the best integrated deep learning model among other classifiers that which includes MLP (73%), LightGBM (90%), XGBoost (91%), Naïve Bayes (34%), AdaBoost (49%), k-NN (74%), logistic regression (43%), LSTM (84%), RF (88%), DT (82%), and SVM (48%).

Keywords: speech emotion recognition; SER; content-based recommendation system; CBRS; movie recommendation; convolutional neural network; CNN.

DOI: 10.1504/IJAISC.2025.151480

International Journal of Artificial Intelligence and Soft Computing, 2025 Vol.9 No.4, pp.379 - 394

Received: 26 Aug 2025
Accepted: 23 Nov 2025
Published online: 02 Feb 2026 *

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Title: An integrated hybrid deep learning framework of speech emotion recognition for personalised movie recommendations

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