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Medientyp:
Bericht;
E-Book
Titel:
Finstreder: simple and fast spoken language understanding with finite state transducers using modern speech-to-text models
Beteiligte:
Bermuth, Daniel
[VerfasserIn];
Poeppel, Alexander
[VerfasserIn];
Reif, Wolfgang
[VerfasserIn]
Erschienen:
Augsburg University Publication Server (OPUS), 2022-11-10
Sprache:
Englisch
DOI:
https://doi.org/10.48550/arXiv.2206.14589
Entstehung:
Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
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.