Robust Energy-efficient Audio-based Anger Detection System for Noisy Environments.

Emotional speech recognition (ESR) seeks to identify human emotions by capturing voice attributes, which can be invaluable for understanding speakers’ moods, attitudes, and feelings. This study investigates affective computing for anger recognition in audio signals. We aim to develop a robust and efficient model capable of detecting anger across various real-world scenarios by employing feature extraction, and virtual enlargement techniques. The experimental setup includes multiple feature sets with different computational costs, and virtual enlargement strategies to simulate different environmental conditions. Results show that models trained with augmented data outperform those trained with non-augmented data, highlighting the importance of data augmentation techniques in enhancing model performance and generalization. Furthermore, additional experiments were conducted to confirm the robustness of data augmentation techniques, which consistently demonstrates their positive impact on model performance. Finally, the study contributes to the field of ESR, particularly anger recognition, and its potential applications in violence detection and public safety. The findings demonstrate the effectiveness of feature extraction, virtual enlargement, and data augmentation techniques in improving model performance across various environmental conditions, thereby enhancing the ability to identify anger in audio signals.