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Various neural network-based approaches have been proposed for more robustand accurate voice activity detection (VAD). Manual design of such neuralarchitectures is an error-prone and time-consuming process, which prompted thedevelopment of neural architecture search (NAS) that automatically design andoptimize network architectures. While NAS has been successfully applied toimprove performance in a variety of tasks, it has not yet been exploited in theVAD domain. In this paper, we present the first work that utilizes NASapproaches on the VAD task. To effectively search architectures for the VADtask, we propose a modified macro structure and a new search space with a muchbroader range of operations that includes attention operations. The resultsshow that the network structures found by the propose NAS framework outperformprevious manually designed state-of-the-art VAD models in various noise-addedand real-world-recorded datasets. We also show that the architectures searchedon a particular dataset achieve improved generalization performance on unseenaudio datasets. Our code and models are available athttps://github.com/daniel03c1/NAS_VAD.

NAS-VAD: Neural Architecture Search for Voice Activity Detection