Automated Evaluation of Speaker Performance Using Machine Learning: A Multi-Modal Approach to Analyzing Audio and Video Features

Harshavardhan Mogalgiddi; Prashant Kumar Dey

Transparent Peer Review By Scholar9

Automated Evaluation of Speaker Performance Using Machine Learning: A Multi-Modal Approach to Analyzing Audio and Video Features

Abstract

In this paper, we propose a novel framework for evaluating the speaking quality of educators using machine learning techniques. Our approach integrates both audio and video data, leveraging key features such as facial expressions, gestures, speech pitch, volume, and pace to assess the overall effectiveness of a speaker. We collect and process data from a set of recorded teaching sessions, where we extract a variety of features using advanced tools such as Amazon Rekognition for video analysis and AWS S3 for speech-to-text conversion. The framework then utilizes a variety of machine learning models, including Logistic Regression, K-Nearest Neighbors, Naive Bayes, Decision Trees, and Support Vector Machines, to classify speakers as either "Good" or "Bad" based on predefined quality indicators. The classification is further refined through feature extraction, where key metrics such as eye contact, emotional states, speech patterns, and question engagement are quantified. After a thorough analysis of the dataset, we apply hyperparameter optimization and evaluate the models using ROC-AUC scores to determine the most accurate predictor of speaker quality. The results demonstrate that Random Forest and Support Vector Machines offer the highest classification accuracy, achieving an ROC-AUC score of 0.89. This research provides a comprehensive methodology for automated speaker evaluation, which could be utilized in various educational and training environments to improve speaker performance.

Balaji Govindarajan Reviewer

Review Request Accepted

Balaji Govindarajan Reviewer

Approved Rating

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

Comment

Relevance and Originality:

This research article addresses a relevant and innovative application of machine learning—evaluating educators' speaking quality. As education increasingly shifts toward online and digital platforms, tools that assess and enhance teaching effectiveness are in high demand. The novelty of the framework lies in its integration of both audio and video data to provide a more holistic assessment of speaker quality. By leveraging advanced tools such as Amazon Rekognition and AWS S3, along with machine learning models, the research offers an original and modern approach to improving educational outcomes through technology.

Methodology:

The study employs a rigorous methodology, combining data collection from recorded teaching sessions, feature extraction, and machine learning model evaluation. The use of multiple models, including Logistic Regression, K-Nearest Neighbors, and Support Vector Machines, provides a robust comparative framework. Additionally, the application of hyperparameter optimization ensures that the models are fine-tuned for accuracy. However, further details on the dataset—such as the size, diversity, and representativeness of the recorded sessions—would enhance the transparency and replicability of the methodology.

Validity & Reliability:

The findings of the study appear valid, particularly given the use of advanced machine learning techniques and tools to assess speaker quality. The use of ROC-AUC scores to evaluate model performance ensures reliable results. However, the reliability could be strengthened by validating the framework with more diverse datasets, including different teaching styles, subjects, and classroom environments. Moreover, incorporating real-world feedback from educators or educational institutions would provide further credibility to the model’s classification accuracy.

Clarity and Structure:

The article is well-structured, with a clear flow from problem identification to the proposed solution, methodology, and results. The explanation of the machine learning models and the process of feature extraction is thorough and easy to follow. However, simplifying technical jargon, particularly for readers unfamiliar with machine learning concepts, would improve accessibility. A more concise presentation of the hyperparameter optimization process could also enhance clarity without compromising on the technical depth.

Result Analysis:

The result analysis is solid, highlighting that Random Forest and Support Vector Machines perform best in classifying speaker quality, with ROC-AUC scores of 0.89. The study effectively demonstrates the strengths of the models and provides a sound rationale for choosing these algorithms. However, the analysis could be improved by discussing potential weaknesses or challenges, such as the need for large datasets to train the models or the variability in speaker styles. Including practical recommendations for deploying the framework in real educational environments would also enrich the result analysis.