# A low latency ASR-free end to end spoken language understanding system

arXiv: https://arxiv.org/abs/2011.04884 · HTML: https://mlsanity.com/paper/2011.04884/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers](https://mlsanity.com/paper/2104.01604/) (2021)
- [A Streaming End-to-End Framework For Spoken Language Understanding](https://mlsanity.com/paper/2105.10042/) (2021)
- [Sequential End-to-End Intent and Slot Label Classification and Localization](https://mlsanity.com/paper/2106.04660/) (2021)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)

## Abstract (arXiv)

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.