Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
This addresses the need for private-by-design voice interfaces on resource-constrained devices, offering a domain-specific solution.
The paper tackles the problem of performing Spoken Language Understanding on IoT devices by presenting the Snips Voice Platform, which achieves fast and accurate embedded inference while enforcing privacy by design without collecting personal user data.
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 collected. 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.