Machine learning approaches to analyzing public speaking and vocal delivery

Abstract

The 21st century has ushered in a wave of technological advancements, notably in machine learning, with profound implications for the analysis of public speaking and vocal delivery. This literature review scrutinizes the deployment of machine learning techniques in the evaluation and enhancement of public speaking skills, a critical facet of effective communication across various professions and everyday contexts.
 The exploration begins with an examination of machine learning models such as Support Vector Machines, Convolutional Neural Networks, and Long Short-Term Memory models. These models' application in the analysis of non-verbal speech features, emotion detection, and performance evaluation offers a promising avenue for objective, scalable, and efficient analysis, surpassing the limitations of traditional, often subjective, methods.
 The discussion extends to the real-world application of these techniques, encompassing public speaking skill analysis, teacher vocal delivery evaluation, and the assessment of public speaking anxiety. Various machine learning frameworks are presented, emphasizing their effectiveness in generating large-scale, objective evaluation results.
 However, the discourse acknowledges the challenges and limitations inherent to these technologies, including data privacy concerns, potential over-reliance on technology, and the necessity for diverse and extensive datasets. The potential drawbacks of these approaches are highlighted, underscoring the need for further research to address these issues.
 Despite these challenges, the successes of numerous machine learning applications in this field are underscored, along with their potential for future advancements. By dissecting past successes and failures, the review aims to provide guidance for the more effective deployment of these technologies in the future, contributing to the ongoing efforts to revolutionize the analysis of public speaking and vocal delivery.