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

• URL: http://arxiv.org/abs/2206.14589v1
• Date: Wed, 29 Jun 2022 12:49:53 GMT
• Title: Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models
• Authors: Daniel Bermuth, Alexander Poeppel, Wolfgang Reif
• Abstract summary: In Spoken Language Understanding (SLU) the task is to extract important information from audio commands. This paper presents a simple method for embedding intents and entities into Finite State Transducers.
• Score: 69.35569554213679
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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