  We present an end-to-end approach to extract semantic concepts directly from
the speech audio signal. To overcome the lack of data available for this spoken
language understanding approach, we investigate the use of a transfer learning
strategy based on the principles of curriculum learning. This approach allows
us to exploit out-of-domain data that can help to prepare a fully neural
architecture. Experiments are carried out on the French MEDIA and PORTMEDIA
corpora and show that this end-to-end SLU approach reaches the best results
ever published on this task. We compare our approach to a classical pipeline
approach that uses ASR, POS tagging, lemmatizer, chunker... and other NLP tools
that aim to enrich ASR outputs that feed an SLU text to concepts system. Last,
we explore the promising capacity of our end-to-end SLU approach to address the
problem of domain portability.
