On Voice Activity Detection for Italian Spoken Language

Abstract

Voice Activity Detection (VAD) refers to the task of identifying human speech in noisy settings, playing a crucial role in fields like speech recognition and audio surveillance. However, most VAD research has predominantly focused on English, leaving other languages — such as Italian —underexplored. This study aims to evaluate and improve VAD systems for Italian speech, with the ultimate goal of enhancing the speech segmentation component of the Digital Linguistic Biomarkers (DLBs) extraction pipeline for early mental disorder screening. We experimented with multiple VAD systems and proposed a novel ensemble approach that demonstrates improved speech event detection performance. This advancement provides a robust foundation for more accurate early detection of mental health conditions using DLBs in the Italian language.