  This paper introduces a new open source platform for end-to-end 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.
