  This work investigates spoken language understanding (SLU) systems in the
scenario when the semantic information is extracted directly from the speech
signal by means of a single end-to-end neural network model. Two SLU tasks are
considered: named entity recognition (NER) and semantic slot filling (SF). For
these tasks, in order to improve the model performance, we explore various
techniques including speaker adaptation, a modification of the connectionist
temporal classification (CTC) training criterion, and sequential pretraining.
