Real-time Speech-based Intoxication Detection System: Vowel Biomarker Analysis with Artificial Neural Networks

Abstract

Alcohol consumption can lead to vocal health risks and long-term health issues for individuals. The paper introduces a novel dataset that analyzes vowel vocalizations to detect early alcohol consumption. This study examines hidden parameters in vowel sounds, such as frequency, jitters, shimmer, and harmonic ratio, which can identify individuals who consume alcohol. It aims to identify subtle vocal patterns that serve as markers for alcohol consumption. This study analyzed 509 vowel vocalizations from 290 records of 46 alcohol-consuming individuals and 219 non-drinkers aged 22-34. The study used intelligent machine learning models and Incremental Hidden Layer Neurons Artificial Neural Networks (IHLN-ANNs) with Back-propagation to identify patterns indicative of alcohol consumption. The Random Forest (RF) model achieved 95.3% accuracy, while the BP-ANNs model showed 99.4% accuracy with five neurons in a hidden layer. The findings could be applied to developing smartphone applications to provide timely alerts and cautionary measures for alcohol consumption, reducing accident risks. The study highlights voice analysis's potential as a non-invasive and cost-effective tool for identifying alcohol consumers, offering potential avenues for future public health initiatives.