Speech Emotion Recognition in Noisy Real-World Environments: Challenges, Applications, Metrics, And Comparative Approaches

Speech Emotion Recognition (SER) is an essential component of affective computing and human-computer interaction, yet its deployment in acoustically adverse environments remains challenging. This paper critically examines the transition of SER systems from controlled laboratory settings to real-world scenarios characterized by diverse noise profiles—such as urban public spaces, vehicular interiors, and call center infrastructures. Key challenges addressed include acoustic feature degradation under noise, the domain shift problem, variability in language and speaker characteristics, and the demand for low-latency processing. We analyze context-specific constraints across multiple applications, including social robotics, driver assistance systems, and contact center analytics. Performance evaluation is discussed through a multi-metric lens, incorporating recognition accuracy (e.g., UAR, WER) and intelligibility scores (e.g., STOI, PESQ). A comparative synthesis of existing models—ranging from traditional classifiers to deep learning architectures such as CNNs, LSTMs, and Transformers—is presented. Particular attention is paid to methods enhancing robustness via data augmentation, speech enhancement, and domain adaptation. Drawing upon literature from 2020 to 2025 and foundational studies, this work offers a structured review and identifies future directions for resilient and scalable SER system design.