The 2020 ESPnet update: New features, broadened applications, performance improvements, and future plans

Shinji Watanabe, Florian Boyer, Xuankai Chang, Pengcheng Guo, Tomoki Hayashi, Yosuke Higuchi, Takaaki Hori, Wen Chin Huang, Hirofumi Inaguma, Naoyuki Kamo, Shigeki Karita, Chenda Li, Jing Shi, Aswin Shanmugam Subramanian, Wangyou Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to mainly deal with end-to-end speech recognition experiments based on sequence-to-sequence modeling. The project has grown rapidly and now covers a wide range of speech processing applications. Now ESPnet also includes text to speech (TTS), voice conversation (VC), speech translation (ST), and speech enhancement (SE) with support for beamforming, speech separation, denoising, and dereverberation. All applications are trained in an end-to-end manner, thanks to the generic sequence to sequence modeling properties, and they can be further integrated and jointly optimized. Also, ESPnet provides reproducible all-in-one recipes for these applications with state-of-the-art performance in various benchmarks by incorporating transformer, advanced data augmentation, and conformer. This project aims to provide up-to-date speech processing experience to the community so that researchers in academia and various industry scales can develop their technologies collaboratively.

Original languageEnglish
Title of host publication2021 IEEE Data Science and Learning Workshop, DSLW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665428255
DOIs
Publication statusPublished - 2021 Jun 5
Externally publishedYes
Event2021 IEEE Data Science and Learning Workshop, DSLW 2021 - Toronto, Canada
Duration: 2021 Jun 52021 Jun 6

Publication series

Name2021 IEEE Data Science and Learning Workshop, DSLW 2021

Conference

Conference2021 IEEE Data Science and Learning Workshop, DSLW 2021
Country/TerritoryCanada
CityToronto
Period21/6/521/6/6

Keywords

  • End-to-end neural network
  • Speech enhancement
  • Speech recognition
  • Speech translation
  • Text-to-speech

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Education

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