no code implementations • 25 Feb 2020 • Théodore Bluche, Maël Primet, Thibault Gisselbrecht
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.
2 code implementations • 30 Oct 2018 • Alaa Saade, Alice Coucke, Alexandre Caulier, Joseph Dureau, Adrien Ball, Théodore Bluche, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet
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
15 code implementations • 25 May 2018 • Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, Joseph Dureau
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 #43 on Speech Recognition on LibriSpeech test-other
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • NeurIPS 2016 • Théodore Bluche
Offline handwriting recognition systems require cropped text line images for both training and recognition.
no code implementations • 12 Apr 2016 • Théodore Bluche, Jérôme Louradour, Ronaldo Messina
We present an attention-based model for end-to-end handwriting recognition.
no code implementations • 5 Nov 2013 • Vu Pham, Théodore Bluche, Christopher Kermorvant, Jérôme Louradour
Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition.