Quantitative Laughter Detection, Measurement, and Classification-A Critical Survey.

Area of Science:

• Computational Social Science
• Human-Computer Interaction
• Neuroscience
• Psychiatry

Background:

• Human nonverbal social behaviors are increasingly studied using quantitative and computational methods.
• Laughter, a complex vocal signal, is a key nonverbal behavior with diverse triggers and functions.
• Multidisciplinary research on laughter lacks a unified approach, leading to heterogeneous methods and contradictory findings.

Purpose of the Study:

• To consolidate objective measurement methods and findings on laughter from various scientific disciplines.
• To address the heterogeneity in laughter analysis, classification, and terminology.
• To contribute to the development of a unified model and taxonomy of laughter.

Main Methods:

• Systematic survey of existing literature on laughter.
• Collection and presentation of objective measurement techniques and empirical results.
• Analysis of diverse studies across multiple research fields.

Main Results:

• Identified a wide range of methods for analyzing laughter across disciplines.
• Documented heterogeneous approaches to laughter classification and terminology.
• Highlighted the need for standardized methodologies and a unified framework.

Conclusions:

• A unified model and taxonomy of laughter are crucial for advancing research.
• Standardized approaches will benefit fields such as artificial intelligence, human-robot interaction, medicine, and psychiatry.
• This survey provides a foundation for future integrated research on laughter.