Search Results for author:
Found 6 papers, 4 papers with code
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.
Ranked #46 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Efficient keyword spotting using dilated convolutions and gating
We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states.
Federated Learning for Keyword Spotting
We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors.
Spoken Language Understanding on the Edge
We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications.
Ranked #4 on
Spoken Language Understanding
on Snips-SmartSpeaker
Predicting detection filters for small footprint open-vocabulary keyword spotting
In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, that allows the users to include a customizable voice interface to their device and that does not require task-specific data.
Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks
We explore a keyword-based spoken language understanding system, in which the intent of the user can directly be derived from the detection of a sequence of keywords in the query.
