# LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection

arXiv: https://arxiv.org/abs/2512.17281 · HTML: https://mlsanity.com/paper/2512.17281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 21 of 21)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [End-to-End Neural Speaker Diarization with Permutation-Free Objectives](https://mlsanity.com/paper/1909.05952/) (2019)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [Keyword Transformer: A Self-Attention Model for Keyword Spotting](https://mlsanity.com/paper/2104.00769/) (2021)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [Towards End-to-end Unsupervised Speech Recognition](https://mlsanity.com/paper/2204.02492/) (2022)
- [rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method](https://mlsanity.com/paper/1906.03588/) (2019)
- [Voices Obscured in Complex Environmental Settings (VOICES) corpus](https://mlsanity.com/paper/1804.05053/) (2018)
- [Personal VAD: Speaker-Conditioned Voice Activity Detection](https://mlsanity.com/paper/1908.04284/) (2019)
- [Analyzing the Robustness of Unsupervised Speech Recognition](https://mlsanity.com/paper/2110.03509/) (2021)
- [Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions](https://mlsanity.com/paper/2312.16613/) (2023)
- [Single-Microphone Speaker Separation and Voice Activity Detection in Noisy and Reverberant Environments](https://mlsanity.com/paper/2401.03448/) (2024)
- [Waveform-based Voice Activity Detection Exploiting Fully Convolutional networks with Multi-Branched Encoders](https://mlsanity.com/paper/2006.11139/) (2020)
- [Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic Delay](https://mlsanity.com/paper/2207.01691/) (2022)
- [Fearless Steps Challenge Phase-1 Evaluation Plan](https://mlsanity.com/paper/2211.02051/) (2022)
- [Personalizing Keyword Spotting with Speaker Information](https://mlsanity.com/paper/2311.03419/) (2023)
- [Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching](https://mlsanity.com/paper/2409.04859/) (2024)
- [Simultaneous Diarization and Separation of Meetings through the Integration of Statistical Mixture Models](https://mlsanity.com/paper/2410.21455/) (2024)

## Abstract (arXiv)

Robust Voice Activity Detection (VAD) remains a challenging task, especially under noisy, diverse, and unseen acoustic conditions. Beyond algorithmic development, a key limitation in advancing VAD research is the lack of large-scale, systematically controlled, and publicly available datasets. To address this, we introduce LibriVAD - a scalable open-source dataset derived from LibriSpeech and augmented with diverse real-world and synthetic noise sources. LibriVAD enables systematic control over speech-to-noise ratio, silence-to-speech ratio (SSR), and noise diversity, and is released in three sizes (15 GB, 150 GB, and 1.5 TB) with two variants (LibriVAD-NonConcat and LibriVAD-Concat) to support different experimental setups. We benchmark multiple feature-model combinations, including waveform, Mel-Frequency Cepstral Coefficients (MFCC), and Gammatone filter bank cepstral coefficients, and introduce the Vision Transformer (ViT) architecture for VAD. Our experiments show that ViT with MFCC features consistently outperforms established VAD models such as boosted deep neural network and convolutional long short-term memory deep neural network across seen, unseen, and out-of-distribution (OOD) conditions, including evaluation on the real-world VOiCES dataset. We further analyze the impact of dataset size and SSR on model generalization, experimentally showing that scaling up dataset size and balancing SSR noticeably and consistently enhance VAD performance under OOD conditions. All datasets, trained models, and code are publicly released to foster reproducibility and accelerate progress in VAD research.