A Novel Labeled Human Voice Signal Dataset for Misbehavior Detection
This work addresses the need for context-aware voice recognition technologies in automated systems, but it is incremental as it focuses on dataset creation without novel methods or performance gains.
The study tackled the problem of classifying voice signals based on human behaviors by collecting a real-time dataset with participants speaking in harsh (misbehaved) and polite (normal) manners, resulting in a labeled dataset for misbehavior detection.
Voice signal classification based on human behaviours involves analyzing various aspects of speech patterns and delivery styles. In this study, a real-time dataset collection is performed where participants are instructed to speak twelve psychology questions in two distinct manners: first, in a harsh voice, which is categorized as "misbehaved"; and second, in a polite manner, categorized as "normal". These classifications are crucial in understanding how different vocal behaviours affect the interpretation and classification of voice signals. This research highlights the significance of voice tone and delivery in automated machine-learning systems for voice analysis and recognition. This research contributes to the broader field of voice signal analysis by elucidating the impact of human behaviour on the perception and categorization of voice signals, thereby enhancing the development of more accurate and context-aware voice recognition technologies.