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arXiv:1805.10190 (cs)
[Submitted on 25 May 2018 (v1), last revised 6 Dec 2018 (this version, v3)]

Title:Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

Authors:Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, Joseph Dureau
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Abstract: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.
Comments: 29 pages, 9 figures, 17 tables
Subjects: Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:1805.10190 [cs.CL]
  (or arXiv:1805.10190v3 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.1805.10190

Submission history

From: Alice Coucke [view email]
[v1] Fri, 25 May 2018 15:04:17 UTC (962 KB)
[v2] Mon, 5 Nov 2018 17:52:08 UTC (968 KB)
[v3] Thu, 6 Dec 2018 16:34:25 UTC (971 KB)
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