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End-to-end named entity and semantic concept extraction from speech

Abstract : Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs. Such approach has some disadvantages (error propagation, metric to tune ASR systems sub-optimal in regards to the final task, reduced space search at the ASR output level,...) and it is known that more integrated approaches outperform sequential ones, when they can be applied. In this paper, we explore an end-to-end approach that directly extracts named entities from speech, though a unique neural architecture. On a such way, a joint optimization is possible for both ASR and NER. Experiments are carried on French data easily accessible, composed of data distributed in several evaluation campaigns. The results are promising since this end-to-end approach provides similar results (F-measure=0.66 on test data) than a classical pipeline approach to detect named entity categories (F-measure=0.64). Last, we also explore this approach applied to semantic concept extraction , through a slot filling task known as a spoken language understanding problem.
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Submitted on : Thursday, June 25, 2020 - 7:31:58 PM
Last modification on : Friday, May 6, 2022 - 3:46:09 AM


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  • HAL Id : hal-01987740, version 2


Sahar Ghannay, Antoine Caubrière, Yannick Estève, Nathalie Camelin, Edwin Simonnet, et al.. End-to-end named entity and semantic concept extraction from speech. IEEE Spoken Language Technology Workshop, Dec 2018, Athens, Greece. ⟨hal-01987740v2⟩



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