  Intent classification is a fundamental task in the spoken language
understanding field that has recently gained the attention of the scientific
community, mainly because of the feasibility of approaching it with end-to-end
neural models. In this way, avoiding using intermediate steps, i.e. automatic
speech recognition, is possible, thus the propagation of errors due to
background noise, spontaneous speech, speaking styles of users, etc. Towards
the development of solutions applicable in real scenarios, it is interesting to
investigate how environmental noise and related noise reduction techniques to
address the intent classification task with end-to-end neural models. In this
paper, we experiment with a noisy version of the fluent speech command data
set, combining the intent classifier with a time-domain speech enhancement
solution based on Wave-U-Net and considering different training strategies.
Experimental results reveal that, for this task, the use of speech enhancement
greatly improves the classification accuracy in noisy conditions, in particular
when the classification model is trained on enhanced signals.
