Reference number : GC-2017-Q4-01607, amendment number : , agreement type : G, recipient legal name : Saucier, Antoine (École Polytechnique de Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Our partner Fluent.AI develops Automatic Speech Recognition (ASR) systems based on neural networks. The_x000D_
performance of ASR systems degrades significantly in the presence of noise. The types of noises that are_x000D_
relevant occur e.g. on urban sidewalks, construction sites, factory floors or in cars (e.g. radio music or_x000D_
background conversations). The performance degradation of ASR systems is partly caused by a lack of_x000D_
robustness to noise of the data representations used in ASR. To improve the performance of our partner's ASR_x000D_
systems, we propose to develop a pre-processing method that reduces noises in the speech signal before it is_x000D_
sent to the ASR engine. Our method is based on independent component analysis., program purpose french : Our partner Fluent.AI develops Automatic Speech Recognition (ASR) systems based on neural networks. The_x000D_
performance of ASR systems degrades significantly in the presence of noise. The types of noises that are_x000D_
relevant occur e.g. on urban sidewalks, construction sites, factory floors or in cars (e.g. radio music or_x000D_
background conversations). The performance degradation of ASR systems is partly caused by a lack of_x000D_
robustness to noise of the data representations used in ASR. To improve the performance of our partner's ASR_x000D_
systems, we propose to develop a pre-processing method that reduces noises in the speech signal before it is_x000D_
sent to the ASR engine. Our method is based on independent component analysis., agreement title english : Front-end Signal Processing for Improved Automatic Speech Recognition, agreement title french : Front-end Signal Processing for Improved Automatic Speech Recognition, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada