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

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