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.

本研究提出使用内容分离的离散单元作为中间指导，以提高文本缺失情况下的口语理解性能，结果表明该方法在五个 SLU 基准语料库上优于基准方法，并且该单位指导可以促进少样本学习，增强模型处理噪声的能力。