Maximizing SER Performance with ICR-SEA: A Novel Framework for Grid-Search Optimization

This proposed work develops a novel strategy to optimize CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory for SER (Speech Emotion Recognition) using Mel-spectrograms as input features with the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) data corpus. Data pre-preparation, including dataset class balancing and augmentation improves model generalization. The hyperparameter tuning strategy employs the Iterative Candidate Refinement with Successive Epoch Adjustment (ICR-SEA) algorithm, which efficiently tunes the CNN-LSTM architecture and training parameters. This approach systematically filters best candidates while dynamically adjusting the number of training epochs, ensuring computational effectiveness. This hyperparameter tuning process investigates changes in convolutional filters, kernel sizes, dense layer sizes, dropout rates, batch sizes, learning rates, and epochs. The top-performing candidate model is precisely evaluated and filtered for the next iteration. The optimized model reaches a classification accuracy of 85.36% approximately during hyperparameter tuning. This research emphasizes the effectiveness of the ICR-SEA algorithm in hyperparameter optimization and the significance of data augmentation in improving the generalizability and performance of emotion recognition systems, setting a benchmark for future work with the RAVDESS dataset.