  This paper presents recent progress on integrating speech separation and
enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE
work, numerous features have been added, including recent state-of-the-art
speech enhancement models with their respective training and evaluation
recipes. Importantly, a new interface has been designed to flexibly combine
speech enhancement front-ends with other tasks, including automatic speech
recognition (ASR), speech translation (ST), and spoken language understanding
(SLU). To showcase such integration, we performed experiments on carefully
designed synthetic datasets for noisy-reverberant multi-channel ST and SLU
tasks, which can be used as benchmark corpora for future research. In addition
to these new tasks, we also use CHiME-4 and WSJ0-2Mix to benchmark multi- and
single-channel SE approaches. Results show that the integration of SE
front-ends with back-end tasks is a promising research direction even for tasks
besides ASR, especially in the multi-channel scenario. The code is available
online at https://github.com/ESPnet/ESPnet. The multi-channel ST and SLU
datasets, which are another contribution of this work, are released on
HuggingFace.
