Finstreder: simple and fast spoken language understanding with finite state transducers using modern speech-to-text models

  • In Spoken Language Understanding (SLU) the task is to extract important information from audio commands, like the intent of what a user wants the system to do and special entities like locations or numbers. This paper presents a simple method for embedding intents and entities into Finite State Transducers, and, in combination with a pretrained general-purpose Speech-to-Text model, allows building SLU-models without any additional training. Building those models is very fast and only takes a few seconds. It is also completely language independent. With a comparison on different benchmarks it is shown that this method can outperform multiple other, more resource demanding SLU approaches.

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Metadaten
Author:Daniel BermuthGND, Alexander PoeppelGND, Wolfgang ReifORCiDGND
URN:urn:nbn:de:bvb:384-opus4-992193
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/99219
Parent Title (English):arXiv
Publisher:arXiv
Type:Preprint
Language:English
Year of first Publication:2022
Publishing Institution:Universität Augsburg
Release Date:2022/11/10
First Page:arXiv:2206.14589
DOI:https://doi.org/10.48550/arXiv.2206.14589
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Software & Systems Engineering
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Lehrstuhl für Softwaretechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-SA 4.0: Creative Commons: Namensnennung - Weitergabe unter gleichen Bedingungen (mit Print on Demand)