preprint وصول مفتوح

ESPnet: End-to-End Speech Processing Toolkit

  • arXiv (Cornell University)
  • Cornell University
Research footprint

At a glance

الاستشهادات
74
المراجع
36
Comments
0
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.

Record transparency

Publication details

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

Comments

تسجيل الدخول للانضمام إلى النقاش.

  1. لا توجد تعليقات بعد. ابدأ النقاش.