  As Automatic Speech Processing (ASR) systems are getting better, there is an
increasing interest of using the ASR output to do downstream Natural Language
Processing (NLP) tasks. However, there are few open source toolkits that can be
used to generate reproducible results on different Spoken Language
Understanding (SLU) benchmarks. Hence, there is a need to build an open source
standard that can be used to have a faster start into SLU research. We present
ESPnet-SLU, which is designed for quick development of spoken language
understanding in a single framework. ESPnet-SLU is a project inside end-to-end
speech processing toolkit, ESPnet, which is a widely used open-source standard
for various speech processing tasks like ASR, Text to Speech (TTS) and Speech
Translation (ST). We enhance the toolkit to provide implementations for various
SLU benchmarks that enable researchers to seamlessly mix-and-match different
ASR and NLU models. We also provide pretrained models with intensively tuned
hyper-parameters that can match or even outperform the current state-of-the-art
performances. The toolkit is publicly available at
https://github.com/espnet/espnet.
