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.

基于人类行为的声音信号分类涉及到对语音模式和发言风格的各个方面进行分析。本研究进行了实时数据集收集，参与者被要求以两种不同的方式回答十二个心理学问题：一种是粗鲁的声音（被归类为 “不良行为”），另一种是礼貌的声音（被归类为 “正常行为”）。这些分类在理解不同声音行为如何影响对声音信号的解释和分类方面至关重要。本研究突出了声音音调和发言方式对自动化机器学习系统在声音分析和识别方面的重要性。通过阐明人类行为对声音信号的感知和分类的影响，本研究为更准确和具有上下文感知能力的声音识别技术的开发做出了贡献。