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.

本文提出了一种语音理解系统，可以将一个波形分类为结构化数据 (如意图和槽) 而不需要先将其转录为文本，同时系统具有足够小的占用空间可以运行在微型控制器和嵌入式系统上，并且具有最小延迟以逐段处理输入音频数据，并得到了优秀的性能表现 (比其他类似任务的公开研究成果要好)，同时具有低延迟和更小的模型。