• Corpus ID: 238583665

A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation

@article{Higuchi2021ACS,
  title={A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation},
  author={Yosuke Higuchi and Nanxin Chen and Yuya Fujita and Hirofumi Inaguma and Tatsuya Komatsu and Jaesong Lee and Jumon Nozaki and Tianzi Wang and Shinji Watanabe},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.05249}
}
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines. Showing great potential for real-time applications, an increasing number of NAR models have been explored in different fields to mitigate the performance gap against AR models. In this work, we conduct a comparative study of various NAR modeling methods for end-to-end automatic speech… 

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