Deep Learning-Based Speech Emotion Recognition for IoT Edge Devices: A Comparative Study

With advancements in artificial intelligence (AI), particularly in pattern recognition, significant progress has been made in recognising human emotions from speech characteristics, facial activity, and physiological responses. However, the expansion of Internet of Things (IoT)-based infrastructures has increased pressure on conventional cloud systems due to the high volume of transmitted data and the need for real-time responsiveness. As a remedy, edge computing has emerged as a distributed alternative, enabling localised data processing and reducing dependency on remote servers. In this context, the present study evaluates the classification performance of three hybrid deep learning (DL) models—Convolutional Neural Network–Dense Neural Network (CNN-Dense), Long Short-Term Memory–Convolutional Neural Network (LSTM-CNN), and Dense–Long Short-Term Memory (Dense-LSTM) —within a simulated edge-based environment. The Toronto Emotional Speech Set (TESS) dataset was employed, and experimental workflows were implemented via Amazon Web Services (AWS) to simulate edge resource limitations. Accuracy was assessed using macro-averaged metrics, including precision, recall, and F1-score. Among the models, CNN-Dense showed the highest performance, achieving an F1-score of 96%, followed by LSTM-CNN (95%) and Dense-LSTM (93%). The findings suggest that CNN–Dense may offer feature extraction advantages, and that hybrid models could be promising for emotion classification in decentralised systems.