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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