Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness

TL;DR

This paper provides a comprehensive comparative analysis of personalized voice activity detection systems, evaluating their real-world effectiveness using diverse performance metrics and extensive experimentation.

Contribution

It introduces a thorough evaluation framework for PVAD systems, highlighting their strengths and limitations in practical scenarios.

Findings

PVAD systems vary significantly in accuracy and latency.

User-level analysis reveals personalized systems improve detection in real-world conditions.

The study identifies key factors influencing PVAD performance.

Abstract

Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need for effective personalized VAD systems has become paramount. In this paper, we present a comparative analysis of Personalized Voice Activity Detection (PVAD) systems to assess their real-world effectiveness. We introduce a comprehensive approach to assess PVAD systems, incorporating various performance metrics such as frame-level and utterance-level error rates, detection latency and accuracy, alongside user-level analysis. Through extensive experimentation and evaluation, we provide a thorough understanding of the strengths and limitations of various PVAD variants. This paper advances the understanding of PVAD technology by offering insights into its efficacy and viability in practical applications using a comprehensive set of metrics.