LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces LibriVAD, a scalable open-source dataset for voice activity detection, and benchmarks a novel application of the Vision Transformer architecture, demonstrating significant improvements over existing models. The comprehensive methodology and rigorous experimental evaluation position this work as a meaningful contribution to the field of machine learning and audio processing.
Methodology
The paper presents a comprehensive methodology for creating the LibriVAD dataset, which includes systematic control over various acoustic conditions, such as SNR and SSR. The introduction of the Vision Transformer (ViT) for VAD tasks is a significant methodological innovation, leveraging its ability to model long-range dependencies. The feature extraction methods are well-defined, and the benchmarking against established models provides a solid foundation for evaluating the proposed approach.
Results & evaluation
The experiments are rigorously designed, utilizing multiple dataset sizes and configurations to assess the performance of the ViT architecture against conventional VAD models. The results demonstrate the effectiveness of the proposed dataset and model, with clear metrics provided (AUC, EER, MinDCF) that validate the findings. The evaluation on the VOiCES dataset further enhances the robustness of the results.
Reproducibility
The authors have made a commendable effort to ensure reproducibility by publicly releasing the dataset, trained models, and code. This transparency is crucial for fostering further research in VAD and related fields. The detailed experimental setup and evaluation metrics also contribute to the reproducibility of the results.
Limitations
While the dataset is extensive, it may still be limited in terms of diversity across different languages, accents, and speech styles, which could affect the generalizability of the VAD systems. Additionally, the performance on out-of-distribution data, while promising, may require further validation across a wider range of real-world conditions.
Broader impact
The introduction of LibriVAD has the potential to significantly advance research in voice activity detection and related areas of speech processing. By providing a large-scale, publicly available dataset, it opens avenues for developing more robust VAD systems that can operate effectively in diverse acoustic environments. This could lead to improvements in applications such as automatic speech recognition, speaker identification, and other speech-related technologies. The paper introduces LibriVAD, a scalable open-source dataset for voice activity detection, and benchmarks a novel application of the Vision Transformer architecture, demonstrating significant improvements over existing models. The comprehensive methodology and rigorous experimental evaluation position this work as a meaningful contribution to the field of machine learning and audio processing.
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