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

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%).