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.

Srinivasulu Harshavardhan Kendyala Reviewer
16 Oct 2024 03:13 PM

Review Request Accepted

Srinivasulu Harshavardhan Kendyala Reviewer
16 Oct 2024 03:14 PM

Not Approved Rating

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

Comment

Relevance and Originality:

This research addresses a significant gap in the evaluation of educators' speaking quality, an essential aspect of effective teaching and learning. The integration of both audio and video data for comprehensive assessment is an innovative approach, particularly in an era where digital learning environments are becoming increasingly prevalent. The originality of the proposed framework lies in its multi-faceted evaluation criteria, which encompass various key features such as facial expressions, gestures, and speech patterns. This holistic methodology sets it apart from traditional assessment methods that often rely on subjective judgments.

Methodology:

The methodology employed in this study is robust, utilizing advanced tools such as Amazon Rekognition and AWS S3 to extract features from recorded teaching sessions. The combination of machine learning models—including Logistic Regression, K-Nearest Neighbors, and Support Vector Machines—provides a solid foundation for classifying educators' speaking quality. However, further elaboration on the dataset's characteristics, such as size and diversity, as well as the criteria for selecting the predefined quality indicators, would enhance the methodological clarity. Additionally, a more detailed explanation of the hyperparameter optimization process would contribute to the reproducibility of the study.

Validity & Reliability:

The validity of the framework is strengthened by its reliance on objective data sources and a comprehensive set of features for evaluation. By employing various machine learning models and comparing their performance through ROC-AUC scores, the study effectively addresses potential biases and enhances reliability. Nonetheless, the paper would benefit from discussing the potential limitations of the chosen features and models, as well as how they might impact the results. Providing insights into the training and testing processes used to validate the model would also add depth to the discussion of reliability.

Clarity and Structure:

The paper is well-structured, guiding the reader through the rationale, methodology, and results in a logical flow. The use of technical terminology is appropriate for the intended audience, but clarifying some concepts for broader accessibility could enhance understanding. Visual aids, such as flowcharts or graphs depicting the feature extraction process and model performance, would greatly improve clarity. Additionally, a summary of key findings at the end of each section could reinforce the main points and aid retention.

Result Analysis:

The result analysis presents a clear demonstration of the framework's effectiveness, with Random Forest and Support Vector Machines achieving an impressive ROC-AUC score of 0.89. This quantitative assessment highlights the potential of the proposed framework in accurately classifying educators' speaking quality. To enhance this section, including comparative performance metrics from other models would provide a more comprehensive view of the results. Furthermore, discussing the implications of these findings for educational practices and potential areas for future research would enrich the analysis and highlight the framework's practical significance in improving educator performance.