Subventions et des contributions :

Titre :
Low-Complexity Speech Recognition for Next Generation Vocal User Interfaces
Numéro de l’entente :
EGP
Valeur d'entente :
25 000,00 $
Date d'entente :
13 déc. 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Québec, Autre, CA
Numéro de référence :
GC-2017-Q3-00438
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Nom légal du bénéficiaire :
Gross, Warren (Université McGill)
Programme :
Subventions d'engagement partenarial pour les universités
But du programme :

Artificial intelligence aims to improve the quality of lives of Canadians in many areas such as health care,x000D
information technology, entertainment and industrial automation. Recent advances in machine learning, inx000D
particular deep neural networks, have been shown to provide the best-known solutions to many challengingx000D
problems in image and speech processing. In particular, Fluent.ai, a Montreal-based R&D company, hasx000D
developed a deep learning algorithm for an entirely acoustic voice interface - converting speech data to actionsx000D
directly, eliminating the costly two-step process of first converting speech to text and then converting text tox000D
action. Fluent.ai would like the McGill team to investigate low-complexity implementation of directx000D
speech-to-action machine learning algorithms in constrained-complexity systems. This research project will bex000D
used to assist Fluent.ai in evaluating the applicability of low-complexity neural networks for their customerx000D
needs in products such as smart toys and personal digital assistants.