WPI - Computer Science Department , MS Thesis Presentation Abigail Rachel Albuquerque, "Detecting Intoxication from Speech using Transformer-based Self-Supervised Learning with Pre-Training"

Abigail Rachel Albuquerque

MS Student

WPI – Computer Science 

Friday, December 6, 2024

Time: 1:00 p.m. – 2:00 p.m.

Location: Rubin Campus Center - Chair’s Conference Room

Zoom: https://wpi.zoom.us/j/8328872674

Advisor: Prof. Emmanuel Agu

Reader: Prof. Fabricio Murai

Abstract:

Alcohol inebriation is a major global cause of fatalities, contributing to over 30% of vehicular accidents. Current methods to prevent Driving Under the Influence (DUI) are often expensive or require external devices. Intoxication detection from speech offers an alternative through continuous monitoring and non-invasive analyses methods. This thesis explores Wav2Vec 2.0, a Transformer-based self-supervised model, for classifying alcohol intoxication from raw audio. 

Challenges addressed by this study include severe class imbalance and inadequate data, which were addressed using resampling techniques, and model fine-tuning after pre-training on larger and more diverse datasets. During rigorous evaluation, the proposed model achieved an Unweighted Average Recall of 73.3%, outperforming state-of-the-art baseline models, highlighting its potential for accurate DUI detection to prevent alcohol-related incidents.