Voice Activity Detection on Italian Language
Authors: Shibingfeng Zhang, Gloria Gagliardi, Fabio Tamburini (Università di Bologna, 2024)
Topic: Multimodal

Abstract
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.

Introduction
Voice Activity Detection (VAD) refers to the task of identifying the presence of human voice activity in noisy speech, classifying utterance segments as “speech” or “non-speech”. Typically, it involves making binary decisions on each frame of a noisy signal [1]. VAD has a wide range of applications, serving as a crucial component in various fields such as telecommunications, speech recognition systems, and audio surveillance. Nevertheless, the great majority of current works focus on the applicationof VAD to English while there are many aspects that can affect the performance of transferring a VAD system from one language to another, potentially leading to suboptimal results. For instance, voice onset time may vary significantly between languages, affecting the system’s ability to detect speech activity accurately [2]. Additionally, differences in phonetic structures can further complicate the system’s effectiveness across languages. Given these factors, conducting research to evaluate various VAD systems on Italian speech would be highly valuable. Digital Linguistic Biomarkers (DLBs) indicate linguistic features automatically extracted directly from patients’ verbal productions that provide insights into their medical state [3]. Gagliardi and Tamburini [3] proposed the first DLBs extraction pipeline for the early diagnosis of mental disorders in Italian. The extraction of acoustic and rhythmic features relies heavily on the preprocessing step which consists of speech segmentation via VAD. The VAD system adopted by Gagliardi and Tamburini [3] is a statistical VAD system named “SSVAD v1.0” [4], which will be presented and compared to other VAD Systems in Section 2. In this project, we focus on VAD for the Italian language, an area that remains largely unexplored, aiming to find a VAD system that performs better and is more reliable than the one adopted in the original pipeline. The outcomes of this project will serve as a fundamental component in the pipeline for extracting DLBs and replacing the current VAD system. Moreover, our efforts will provide a robust foundation for future work in this domain, facilitating more accurate and early detection of mental health issues using linguistic biomarkers. Our main contributions are as follows: -TEsting and evaluating various VAD systems on Italian speech. - Proposing an ensemble VAD system that achieves superior results. This paper is structured into five sections. Section 2 presents the data resources and VAD systems leveraged in this work. Section 3 details the experiments and resources for testing VAD systems. Section 4 presents and discusses the experimental results. Finally, Section 5 draws conclusions.

Conclusion
In this study, we explored and enhanced Voice Activity Detection systems for the Italian language, a relatively under-explored area in speech processing. We experimented with various systems and integrated systems into an ensemble to improve detection accuracy. Our findings indicate that combining predictions from multiple models can lead to better results in detecting speech temporal intervals. This effective ensemble method will be used as a component of a Digital Linguistic Biomarkers extraction pipeline. By enhancing the accuracy of speech segmentation, this method provides a more reliable foundation for extracting meaningful linguistic features for the diagnosis of cognitive impairment. Future research could focus on refining the ensemble method by incorporating additional linguistic features into VAD systems and exploring their synergistic effects. Additionally, investigating the application of this approach to other languages and dialects could expand its utility.