Improved Affective Computing via CNN and Bat Algorithm Optimization: A Case Study on IEMOCAP and TESS

This study presents an enhanced Speech Emotion Recognition (SER) model that integrates Convolutional Neural Networks (CNN) with the Bat Algorithm (BA), a nature-inspired metaheuristic optimization technique. The objective is to improve the accuracy and generalizability of emotion classification from speech by optimizing the neural network architecture. The model utilizes handcrafted acoustic features—pitch, energy, zero-crossing rate (ZCR), and Mel-Frequency Cepstral Coefficients (MFCCs)—as inputs. Before being input into a deep neural network whose hyperparameters are modified using the Bat Algorithm, these characteristics are preprocessed and normalized. The last model uses a Gamma Classifier (GC) and Error Correcting Output Codes (ECOC) to guarantee strong categorization. Experimental findings employing benchmark datasets like IEMOCAP, EMO-DB, and Berlin DB show better performance, with validation accuracy climbing as high as 98.44%. This hybrid design offers a consistent method for practical emotion identification systems and outperforms traditional methods.