mlsanityData to 14 Oct 2026

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

arXiv 2206.14589 ↗First posted 29 Jun 2022cs.CL cs.HC cs.SDcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

23 of 40 references.

References · 23

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  2. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
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  10. 2018Spoken Language Understanding on the Edgecited by 27
  11. 2021Speech-language Pre-training for End-to-end Spoken Language Understandingcited by 27
  12. 2019Using Speech Synthesis to Train End-to-End Spoken Language Understanding Modelscited by 24
11 more
  1. 2021Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understandingcited by 22
  2. 2020Improving End-to-End Speech-to-Intent Classification with Reptilecited by 17
  3. 2020Exploring Transfer Learning For End-to-End Spoken Language Understandingcited by 17
  4. 2021End-to-End Spoken Language Understanding for Generalized Voice Assistantscited by 17
  5. 2021Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numberscited by 11
  6. 2021FANS: Fusing ASR and NLU for on-device SLUcited by 8
  7. 2021Sequential End-to-End Intent and Slot Label Classification and Localizationcited by 7
  8. 2021Scribosermo: Fast Speech-to-Text models for German and other Languagescited by 7
  9. 2017Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understandingcited by 6
  10. 2021Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?cited by 5
  11. 2019Sampling from Stochastic Finite Automata with Applications to CTC Decodingcited by 1

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023SpeechPrompt v2: Prompt Tuning for Speech Classification Taskscited by 32
  2. 2023UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructionscited by 9