# rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method

arXiv: https://arxiv.org/abs/1906.03588 · HTML: https://mlsanity.com/paper/1906.03588/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 30 of 34)

- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [Towards End-to-end Unsupervised Speech Recognition](https://mlsanity.com/paper/2204.02492/) (2022)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://mlsanity.com/paper/2303.18110/) (2023)
- [SyllableLM: Learning Coarse Semantic Units for Speech Language Models](https://mlsanity.com/paper/2410.04029/) (2024)
- [Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning](https://mlsanity.com/paper/2204.05148/) (2022)
- [Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2503.10301/) (2025)
- [Voice activity detection in the wild: A data-driven approach using teacher-student training](https://mlsanity.com/paper/2105.04065/) (2021)
- [Impact of annotation modality on label quality and model performance in the automatic assessment of laughter in-the-wild](https://mlsanity.com/paper/2211.00794/) (2022)
- [REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR](https://mlsanity.com/paper/2402.03988/) (2024)
- [Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions](https://mlsanity.com/paper/2312.16613/) (2023)
- [Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models](https://mlsanity.com/paper/2409.07936/) (2024)
- [U-vectors: Generating clusterable speaker embedding from unlabeled data](https://mlsanity.com/paper/2102.03868/) (2021)
- [SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning](https://mlsanity.com/paper/2210.09524/) (2022)
- [SVVAD: Personal Voice Activity Detection for Speaker Verification](https://mlsanity.com/paper/2305.19581/) (2023)
- [Audio-Visual Neural Syntax Acquisition](https://mlsanity.com/paper/2310.07654/) (2023)
- [MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement](https://mlsanity.com/paper/2507.00966/) (2025)
- [No-audio speaking status detection in crowded settings via visual pose-based filtering and wearable acceleration](https://mlsanity.com/paper/2211.00549/) (2022)
- [Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise](https://mlsanity.com/paper/2211.01621/) (2022)
- [REWIND Dataset: Privacy-preserving Speaking Status Segmentation from Multimodal Body Movement Signals in the Wild](https://mlsanity.com/paper/2403.01229/) (2024)
- [Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness](https://mlsanity.com/paper/2406.09443/) (2024)
- [SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models](https://mlsanity.com/paper/2607.05365/) (2026)
- [Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification](https://mlsanity.com/paper/2102.02074/) (2021)
- [On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification](https://mlsanity.com/paper/2201.06426/) (2022)
- [NAS-VAD: Neural Architecture Search for Voice Activity Detection](https://mlsanity.com/paper/2201.09032/) (2022)
- [Nonwords Pronunciation Classification in Language Development Tests for Preschool Children](https://mlsanity.com/paper/2206.08058/) (2022)
- [TCG CREST System Description for the Second DISPLACE Challenge](https://mlsanity.com/paper/2409.15356/) (2024)
- [Grammar Induction from Visual, Speech and Text](https://mlsanity.com/paper/2410.03739/) (2024)
- [Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining](https://mlsanity.com/paper/2501.03184/) (2025)
- [Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion](https://mlsanity.com/paper/2506.01365/) (2025)

## Abstract (arXiv)

This paper presents an unsupervised segment-based method for robust voice activity detection (rVAD). The method consists of two passes of denoising followed by a voice activity detection (VAD) stage. In the first pass, high-energy segments in a speech signal are detected by using a posteriori signal-to-noise ratio (SNR) weighted energy difference and if no pitch is detected within a segment, the segment is considered as a high-energy noise segment and set to zero. In the second pass, the speech signal is denoised by a speech enhancement method, for which several methods are explored. Next, neighbouring frames with pitch are grouped together to form pitch segments, and based on speech statistics, the pitch segments are further extended from both ends in order to include both voiced and unvoiced sounds and likely non-speech parts as well. In the end, a posteriori SNR weighted energy difference is applied to the extended pitch segments of the denoised speech signal for detecting voice activity. We evaluate the VAD performance of the proposed method using two databases, RATS and Aurora-2, which contain a large variety of noise conditions. The rVAD method is further evaluated, in terms of speaker verification performance, on the RedDots 2016 challenge database and its noise-corrupted versions. Experiment results show that rVAD is compared favourably with a number of existing methods. In addition, we present a modified version of rVAD where computationally intensive pitch extraction is replaced by computationally efficient spectral flatness calculation. The modified version significantly reduces the computational complexity at the cost of moderately inferior VAD performance, which is an advantage when processing a large amount of data and running on low resource devices. The source code of rVAD is made publicly available.