A Novel Labeled Human Voice Signal Dataset for Misbehavior Detection

TL;DR

This paper introduces a new labeled dataset of human voice signals capturing different behavioral tones, aiming to improve voice misbehavior detection in machine learning systems.

Contribution

It presents a real-time voice dataset with labeled speech in harsh and polite manners, emphasizing behavioral impact on voice classification.

Findings

Voice tone significantly affects classification accuracy

Dataset enables better understanding of behavioral vocal cues

Improves context-aware voice recognition systems

Abstract

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.