测试不同窗口大小对语音检测精度影响，给出实用优化参考。
Window Size Versus Accuracy Experiments in Voice Activity Detectors
- 对比三种算法在不同窗口下的表现，找出最优配置
- Silero性能显著优于WebRTC和RMS，窗口大小影响明显
- 引入滞后机制可提升WebRTC效果，适合实时系统优化
语音活动检测（VAD）在语音识别等应用中至关重要。本文分析了窗口大小对三种VAD算法——Silero、WebRTC和均方根（RMS）——在多样真实数字音频流上的准确性影响，并进一步研究在每种VAD输出上叠加滞回机制的效果。实验结果为优化VAD系统提供了实用参考。Silero在多数场景下显著优于WebRTC和RMS，而滞回机制对WebRTC有明显增益。
原文摘要 · Abstract (English)
Voice activity detection (VAD) plays a vital role in enabling applications such as speech recognition. We analyze the impact of window size on the accuracy of three VAD algorithms: Silero, WebRTC, and Root Mean Square (RMS) across a set of diverse real-world digital audio streams. We additionally explore the use of hysteresis on top of each VAD output. Our results offer practical references for optimizing VAD systems. Silero significantly outperforms WebRTC and RMS, and hysteresis provides a benefit for WebRTC.
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