Recent Advances in End-to-End Spoken Language Understanding

29 Sep 2019  ·  Natalia Tomashenko, Antoine Caubriere, Yannick Esteve, Antoine Laurent, Emmanuel Morin ·

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.

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