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

doi:10.1109/slt48900.2021.9383615

arXiv · 2011.03706

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

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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.

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BibTeX RIS

Chenda Li, Jing Shi, Wangyou Zhang, Aswin Shanmugam Subramanian, Xuankai Chang, Naoyuki Kamo, Moto Hira, Tomoki Hayashi, Christoph Boeddeker, Zhuo Chen, Shinji Watanabe. 2020-11-07. ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration. https://doi.org/10.1109/slt48900.2021.9383615

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