A Low Latency ASR-Free End to End Spoken Language Understanding System

Mohamed Mhiri, Samuel Myer, Vikrant Singh Tomar


In recent years, developing a speech understanding system that classifies a waveform to structured data, such as intents and slots, without first transcribing the speech to text has emerged as an interesting research problem. This work proposes such as system with an additional constraint of designing a system that has a small enough footprint to run on small micro-controllers and embedded systems with minimal latency. Given a streaming input speech signal, the proposed system can process it segment-by-segment without the need to have the entire stream at the moment of processing. The proposed system is evaluated on the publicly available Fluent Speech Commands dataset. Experiments show that the proposed system yields state-of-the-art performance with the advantage of low latency and a much smaller model when compared to other published works on the same task.


 DOI: 10.21437/Interspeech.2020-1449

Cite as: Mhiri, M., Myer, S., Tomar, V.S. (2020) A Low Latency ASR-Free End to End Spoken Language Understanding System. Proc. Interspeech 2020, 1947-1951, DOI: 10.21437/Interspeech.2020-1449.


@inproceedings{Mhiri2020,
  author={Mohamed Mhiri and Samuel Myer and Vikrant Singh Tomar},
  title={{A Low Latency ASR-Free End to End Spoken Language Understanding System}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={1947--1951},
  doi={10.21437/Interspeech.2020-1449},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1449}
}