  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.
