ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration
| Autor: | Li, Chenda, Shi, Jing, Zhang, Wangyou, Subramanian, Aswin Shanmugam, Chang, Xuankai, Kamo, Naoyuki, Hira, Moto, Hayashi, Tomoki, Boeddeker, Christoph, Chen, Zhuo, Watanabe, Shinji |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/SLT48900.2021.9383615 |
| Popis: | 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. Comment: Accepted by SLT 2021 |
| Databáze: | arXiv |
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