ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration

Chenda Li, Jing Shi, Wangyou Zhang, Aswin Shanmugam Subramanian, Xuankai Chang, Naoyuki Kamo, Moto Hira, Tomoki Hayashi, Christoph Boeddeker, Zhuo Chen, Shinji Watanabe

Research output: Contribution to journalArticlepeer-review

Abstract

We present ESPnet-SE, which is designed for the quick development of speech enhancement and speech separation systems in a single framework, along with the optional downstream speech recognition module. ESPnet-SE is a new project which integrates rich automatic speech recognition related models, resources and systems to support and validate the proposed front-end implementation (i.e. speech enhancement and separation). It is capable of processing both single-channel and multi-channel data, with various functionalities including dereverberation, denoising and source separation. We provide all-in-one recipes including data pre-processing, feature extraction, training and evaluation pipelines for a wide range of benchmark datasets. This paper describes the design of the toolkit, several important functionalities, especially the speech recognition integration, which differentiates ESPnet-SE from other open source toolkits, and experimental results with major benchmark datasets.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2020 Nov 7

Keywords

  • End-to-end
  • Open-source
  • Source separation
  • Speech enhancement
  • Speech recognition

ASJC Scopus subject areas

  • General

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