Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
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发表时间：
2018-05
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通讯作者：
A. Coucke;Alaa Saade;Adrien Ball;Théodore Bluche;A. Caulier;David Leroy;Clément Doumouro;Thibault Gissel
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作者：
A. Coucke;Alaa Saade;Adrien Ball;Théodore Bluche;A. Caulier;David Leroy;Clément Doumouro;Thibault Gissel
本文介绍了 Snips 语音平台的机器学习架构，这是一种在物联网设备典型微处理器上执行口语理解的软件解决方案。嵌入式推理快速而准确，同时通过设计强制执行隐私，因为不会收集任何个人用户数据。我们专注于自动语音识别和自然语言理解，详细介绍了训练高性能机器学习模型的方法，这些模型足够小，可以在小型设备上实时运行。此外，我们描述了一种数据生成过程，可以在不损害用户隐私的情况下提供足够的、高质量的训练数据。
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.