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ESPnet: End-to-End Speech Processing Toolkit

  • arXiv (Cornell University)
  • Cornell University
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Paper overview

Abstract

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.

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Publication details

DOI
10.48550/arxiv.1804.00015
OpenAlex
W2795935804
Document type
preprint
Language
EN
Source
arXiv (Cornell University)
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