  Recently, deep end-to-end learning has been studied for intent classification
in Spoken Language Understanding (SLU). However, end-to-end models require a
large amount of speech data with intent labels, and highly optimized models are
generally sensitive to the inconsistency between the training and evaluation
conditions. Therefore, a natural language understanding approach based on
Automatic Speech Recognition (ASR) remains attractive because it can utilize a
pre-trained general language model and adapt to the mismatch of the speech
input environment. Using this module-based approach, we improve a noisy-channel
model to handle transcription inconsistencies caused by ASR errors. We propose
a two-stage method, Contrastive and Consistency Learning (CCL), that correlates
error patterns between clean and noisy ASR transcripts and emphasizes the
consistency of the latent features of the two transcripts. Experiments on four
benchmark datasets show that CCL outperforms existing methods and improves the
ASR robustness in various noisy environments. Code is available at
https://github.com/syoung7388/CCL.
