  Spoken Language Understanding (SLU) is a task that aims to extract semantic
information from spoken utterances. Previous research has made progress in
end-to-end SLU by using paired speech-text data, such as pre-trained Automatic
Speech Recognition (ASR) models or paired text as intermediate targets.
However, acquiring paired transcripts is expensive and impractical for
unwritten languages. On the other hand, Textless SLU extracts semantic
information from speech without utilizing paired transcripts. However, the
absence of intermediate targets and training guidance for textless SLU often
results in suboptimal performance. In this work, inspired by the
content-disentangled discrete units from self-supervised speech models, we
proposed to use discrete units as intermediate guidance to improve textless SLU
performance. Our method surpasses the baseline method on five SLU benchmark
corpora. Additionally, we find that unit guidance facilitates few-shot learning
and enhances the model's ability to handle noise.
