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

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Speech Recognition LibriSpeech test-clean Snips Word Error Rate (WER) 6.4 # 53
Speech Recognition LibriSpeech test-other Snips Word Error Rate (WER) 16.5 # 47


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