Voice Activity Detection (VAD) refers to the task of identifying human voice activity in noisy settings, playing a crucial role in fields like speech recognition and audio surveillance. However, most VAD research focuses on English, leaving other languages, such as Italian, under-explored. This study aims to evaluate and enhance VAD systems for Italian speech, with the goal of finding a solution for the speech segmentation component of the Digital Linguistic Biomarkers (DLBs) extraction pipeline for early mental disorder diagnosis. We experimented with various VAD systems and proposed an ensemble VAD system. Our ensemble system shows improvements in speech event detection. This advancement lays a robust foundation for more accurate early detection of mental health issues using DLBs in Italian.