Artificial Emotional Intelligence (AEI) has gained significant attention for enabling machines to recognize and interpret human affective states through modalities such as speech. While deep learning-based speech emotion recognition (SER) models have achieved promising accuracy levels, their practical deployment in resource-constrained IoT edge environments remains insufficiently explored. In particular, there is a lack of systematic evaluation approaches that jointly consider classification performance, computational efficiency, and deployment feasibility under edge-oriented operational constraints. In this study, I address this gap by proposing a deployment-aware evaluation perspective for SER systems operating under IoT edge constraints. Rather than introducing a new model architecture, I focus on establishing a unified and reproducible evaluation framework that reflects practical deployment considerations for edge-based intelligent systems. Within this framework, three widely used deep learning architectures, convolutional neural networks (CNN), long short-term memory (LSTM), and dense neural networks, are systematically analyzed using the EMODB dataset. The experimental results demonstrate that CNN-based models achieve the most consistent classification performance, with peak validation accuracy reaching approximately 84%, while also providing a favorable balance between recognition performance and computational efficiency. To better reflect deployment-oriented evaluation, the study also considers latency-related behavior and computational characteristics relevant to edge computing environments based on benchmark-driven estimations. The findings highlight the importance of deployment-aware evaluation strategies and provide practical insights for selecting suitable model architectures in edge-oriented speech emotion recognition scenarios. This study contributes to bridging the gap between theoretical deep learning performance and practical feasibility considerations in IoT-based intelligent systems.
Şükrü Mustafa Kaya (Thu,) studied this question.