  Spoken Language Understanding (SLU) is the problem of extracting the meaning
from speech utterances. It is typically addressed as a two-step problem, where
an Automatic Speech Recognition (ASR) model is employed to convert speech into
text, followed by a Natural Language Understanding (NLU) model to extract
meaning from the decoded text. Recently, end-to-end approaches were emerged,
aiming at unifying the ASR and NLU into a single SLU deep neural architecture,
trained using combinations of ASR and NLU-level recognition units. In this
paper, we explore a set of recurrent architectures for intent classification,
tailored to the recently introduced Fluent Speech Commands (FSC) dataset, where
intents are formed as combinations of three slots (action, object, and
location). We show that by combining deep recurrent architectures with standard
data augmentation, state-of-the-art results can be attained, without using
ASR-level targets or pretrained ASR models. We also investigate its
generalizability to new wordings, and we show that the model can perform
reasonably well on wordings unseen during training.
