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

TL;DR

ESPnet-SE is a comprehensive toolkit for speech enhancement and separation that integrates automatic speech recognition, supporting various functionalities and benchmark datasets for efficient development and validation.

Contribution

It introduces a unified framework combining speech enhancement, separation, and recognition, with multi-channel processing and all-in-one recipes, differentiating it from existing tools.

Findings

Effective processing of single and multi-channel data

Supports dereverberation, denoising, and source separation

Achieves competitive results on benchmark datasets

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.