Attention-Based End-To-End Named Entity Recognition From Speech

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Conference article in proceedings
Date
2021
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Mcode
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Language
en
Pages
12
469 - 480
Series
Text, Speech, and Dialogue - 24th International Conference, TSD 2021, Proceedings, Lecture Notes in Computer Science, Volume 12848
Abstract
Named entities are heavily used in the field of spoken language understanding, which uses speech as an input. The standard way of doing named entity recognition from speech involves a pipeline of two systems, where first the automatic speech recognition system generates the transcripts, and then the named entity recognition system produces the named entity tags from the transcripts. In such cases, automatic speech recognition and named entity recognition systems are trained independently, resulting in the automatic speech recognition branch not being optimized for named entity recognition and vice versa. In this paper, we propose two attention-based approaches for extracting named entities from speech in an end-to-end manner, that show promising results. We compare both attention-based approaches on Finnish, Swedish, and English data sets, underlining their strengths and weaknesses.
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| openaire: EC/H2020/780069/EU//MeMAD
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Citation
Porjazovski , D , Leinonen , J & Kurimo , M 2021 , Attention-Based End-To-End Named Entity Recognition From Speech . in K Ekštein , F Pártl & M Konopík (eds) , Text, Speech, and Dialogue - 24th International Conference, TSD 2021, Proceedings . Lecture Notes in Computer Science , vol. 12848 , Springer , pp. 469 - 480 , International Conference on Text, Speech, and Dialogue , Olomouc , Czech Republic , 06/09/2021 . https://doi.org/10.1007/978-3-030-83527-9_40