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arXiv · 2610.14607

Window Size Matters: Benchmarking Voice Activity Detectors in Noise

Alok Kumar·Mohit Srivastava·Anupam Purwar·Kuldeep Kumar Nigam·Yash Anchaliya·Pranav Wadhwa·Debrup Paul
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Abstract

Voice activity detection (VAD) sits in front of almost every streaming speech recognizer, yet the choice of detector is often made by feel rather than by measurement. We report a frame-level offline benchmark of three detectors on the test-clean split of LibriVAD-small: the neural TEN VAD, the neural Silero VAD and a simple RMS energy baseline. The evaluation covers 2,214 noisy utterances (about 7.1 hours) built from LibriSpeech, nine noise families and six SNR levels from -5 to 20 dB. At a 16 ms hop, TEN VAD reaches a ROC AUC of 0.917, an average precision of 0.968 and an F1 of 0.913. A sweep over 20, 30, 40 and 50 ms windows shows TEN to be almost insensitive to frame length (AUC 0.916 to 0.919), while RMS stays near 0.75 to 0.77. Silero matches TEN only near its native 32 ms input size and drops to 0.63 at 20 ms. We hypothesize that this is likely caused by our fixed-frame protocol rather than by the model itself. Zero padding, sub-chunk averaging and unreset model state are the likely factors, but we have not ablated them. In a per-condition breakdown of TEN, babble noise at low SNR stays close to chance. We also show that F1 at a fixed 0.5 threshold is a weak metric on this data, because a trivial all-speech detector already scores 0.854. All settings needed to reproduce the results are given in the paper.

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Alok Kumar, Mohit Srivastava, Anupam Purwar, Kuldeep Kumar Nigam, Yash Anchaliya, Pranav Wadhwa, Debrup Paul. 2026-10-14. Window Size Matters: Benchmarking Voice Activity Detectors in Noise. https://arxiv.org/abs/2610.14607

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