This paper presents the machine learning architecture of the Snips Voice
Platform, a software solution to perform Spoken Language Understanding on
microprocessors typical of IoT devices. The embedded inference is fast and
accurate while enforcing privacy by design, as no personal user data is ever
Focusing on Automatic Speech Recognition and Natural Language
Understanding, we detail our approach to training high-performance Machine
Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient,
high-quality training data without compromising user privacy.