In competitive debates, the effectiveness of arguments is often assessed through verbal communication. However, nonverbal biometric factors, such as vocal characteristics, play a crucial yet underexplored role in influencing judges’ perceptions and scores. The proposed study explores the role and significance of nonverbal biometric factors in determining the persuasiveness of arguments in competitive debates. The experimental pipeline includes phases such as data collection, analysis of audio features such as Short-Time Fourier Transforms (STFT) and Mel Spectrograms, and utilization of several machine learning algorithms, including Least Squares Linear Regression, Random Forests (RF), Support Vector Machines (SVM), and a Convolutional Neural Network (CNN) to evaluate the usefulness of nonverbal biometrics in predicting judges’ scores. From the existing IBM Debater dataset of recorded speeches, a subset of 72 speeches across 9 speakers was selected and scored by a team of qualified school-level adjudicators to create the dataset used in these experiments. The preliminary results on the dataset were promising and have provided valuable insights into the challenges and efficacy of various regression techniques in audio-based persuasiveness prediction, highlighting the need for further exploration in this domain.