  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.
