ESPnet: End-to-End Speech Processing Toolkit

نویسندگان

  • Shinji Watanabe
  • Takaaki Hori
  • Shigeki Karita
  • Tomoki Hayashi
  • Jiro Nishitoba
  • Yuya Unno
  • Nelson Enrique Yalta Soplin
  • Jahn Heymann
  • Matthew Wiesner
  • Nanxin Chen
  • Adithya Renduchintala
  • Tsubasa Ochiai
چکیده

This paper introduces a new open source platform for end-toend speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.

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تاریخ انتشار 2018