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In Spoken Language Understanding (SLU) the task is to extract importantinformation from audio commands, like the intent of what a user wants thesystem to do and special entities like locations or numbers. This paperpresents a simple method for embedding intents and entities into Finite StateTransducers, and, in combination with a pretrained general-purposeSpeech-to-Text model, allows building SLU-models without any additionaltraining. Building those models is very fast and only takes a few seconds. Itis also completely language independent. With a comparison on differentbenchmarks it is shown that this method can outperform multiple other, moreresource demanding SLU approaches.

Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models