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Media type:
E-Book;
Report
Title:
Finstreder: simple and fast spoken language understanding with finite state transducers using modern speech-to-text models
Contributor:
Bermuth, Daniel
[Author];
Poeppel, Alexander
[Author];
Reif, Wolfgang
[Author]
imprint:
Augsburg University Publication Server (OPUS), 2022-11-10
Language:
English
DOI:
https://doi.org/10.48550/arXiv.2206.14589
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
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.