Théodore Bluche
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Predicting Detection Filters for Small Footprint Open-Vocabulary Keyword Spotting Open
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. We present a keyword detec…
Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks Open
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. In this paper, we focus on an open-vocabulary keyword spott…
Predicting detection filters for small footprint open-vocabulary keyword\n spotting Open
In this paper, we propose a fully-neural approach to open-vocabulary keyword\nspotting, that allows the users to include a customizable voice interface to\ntheir device and that does not require task-specific data. We present a keyword\nde…
Spoken Language Understanding on the Edge Open
We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contributions are twofold. First, we outline the design of an embedded, private-by-design SLU system and show that …
Snips Voice Platform: an embedded Spoken Language Understanding system\n for private-by-design voice interfaces Open
This paper presents the machine learning architecture of the Snips Voice\nPlatform, a software solution to perform Spoken Language Understanding on\nmicroprocessors typical of IoT devices. The embedded inference is fast and\naccurate while…
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Open
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 en…
Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention Open
We present an attention-based model for end-to-end handwriting recognition. Our system does not require any segmentation of the input paragraph. The model is inspired by the differentiable attention models presented recently for speech rec…
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition Open
Offline handwriting recognition systems require cropped text line images for both training and recognition. On the one hand, the annotation of position and transcript at line level is costly to obtain. On the other hand, automatic line seg…