# NAS-VAD: Neural Architecture Search for Voice Activity Detection Source: [NAS-VAD: Neural Architecture Search for Voice Activity Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:37:22.464Z Authors: Daniel Rho, Jinhyeok Park, Jong Hwan Ko Published: 2022-01-22T12:06:41.000Z ## Abstract Various neural network-based approaches have been proposed for more robust and accurate voice activity detection (VAD). Manual design of such neural architectures is an error-prone and time-consuming process, which prompted the development of neural architecture search (NAS) that automatically design and optimize network architectures. While NAS has been successfully applied to improve performance in a variety of tasks, it has not yet been exploited in the VAD domain. In this paper, we present the first work that utilizes NAS approaches on the VAD task. To effectively search architectures for the VAD task, we propose a modified macro structure and a new search space with a much broader range of operations that includes attention operations. The results show that the network structures found by the propose NAS framework outperform previous manually designed state-of-the-art VAD models in various noise-added and real-world-recorded datasets. We also show that the architectures searched on a particular dataset achieve improved generalization performance on unseen audio datasets. Our code and models are available at https://github.com/daniel03c1/NAS\_VAD. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Voice Activity Detection on AVA-Speech (test)](): Voice Activity Detection; AVA-Speech (test); AUC-ROC: 90.5. ## Original sources and implementation [Original paper]() [PDF]()