Efficient Neural Architecture Search for End-to-End Speech Recognition Via Straight-Through Gradients

@article{Zheng2021EfficientNA,
  title={Efficient Neural Architecture Search for End-to-End Speech Recognition Via Straight-Through Gradients},
  author={Huahuan Zheng and Keyu An and Zhijian Ou},
  journal={2021 IEEE Spoken Language Technology Workshop (SLT)},
  year={2021},
  pages={60-67}
}
Neural Architecture Search (NAS), the process of automating architecture engineering, is an appealing next step to advancing end-to-end Automatic Speech Recognition (ASR), replacing expert-designed networks with learned, task-specific architectures. In contrast to early computational-demanding NAS methods, recent gradient-based NAS methods, e.g., DARTS (Differentiable ARchiTecture Search), SNAS (Stochastic NAS) and ProxylessNAS, significantly improve the NAS efficiency. In this paper, we make… 

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