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Recent Advances in End-to-End Spoken Language Understanding

Abstract : This work deals with spoken language understanding (SLU) systems in the scenario when the semantic information is extracted directly from the audio speech signal by means of a single end-to-end neural network model. We consider two SLU tasks: named entity recognition (NER) and semantic slot filling (SF). For these tasks, in order to improve the model performance, we explore various strategies including speaker adaptive training and sequential pretraining schemes.
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Contributor : Yannick Estève <>
Submitted on : Thursday, November 7, 2019 - 10:23:55 AM
Last modification on : Wednesday, November 25, 2020 - 10:28:02 AM
Long-term archiving on: : Saturday, February 8, 2020 - 11:24:19 PM


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  • HAL Id : hal-02353011, version 1


Natalia Tomashenko, Antoine Caubrière, Yannick Estève, Antoine Laurent, Emmanuel Morin. Recent Advances in End-to-End Spoken Language Understanding. 7th International Conference on Statistical Language and Speech Processing (SLSP), Oct 2019, Ljubljana, Slovenia. ⟨hal-02353011⟩



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