The Impact of Acoustic Features on the Persuasiveness of COVID-19 Lifestyle Speeches- Spectral and Cepstral Features

This project explored the persuasive impact of South African COVID-19 lifestyle speeches through a proof-of-concept investigation using eNCA interviews extracted from YouTube. Using generated spectral and cepstral features from interview audio, a neural network compares their impact on persuasiveness. The cepstral feature set outperforms the spectral set, with a balanced accuracy of 86.8% and an F1 of 85.0%, indicating its higher predictive power despite challenges like overfitting and dataset size. This project was completed with Gershon Koral.

Skills:

Database building, data processing, MATLAB, machine learning, signal processing