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UID:20260525T160152EDT-8051aUEB4D@132.216.98.100
DTSTAMP:20260525T200152Z
DESCRIPTION: \n\nAbstract\n\nVoice control systems enable people to control
  their computers by speaking to them. After a review of the state-of-the-a
 rt in sequence modeling\, speech recognition\, and language understanding 
 using deep learning\, this thesis describes a number of contributions to t
 he art of voice control. The first contribution is a study of large-scale 
 semi-supervised learning through pseudo-labeling for massively multilingua
 l speech recognition. The second contribution is a study of the use of aut
 oregressive models for conditional computation with neural networks\, usin
 g speech recognition as a test case. The third contribution is a method fo
 r training end-to-end spoken language understanding models using speech sy
 nthesis. The fourth contribution is a crowdsourced dataset\, Timers and Su
 ch\, for spoken language understanding involving numbers\, along with base
 line experimental results and open-source software infrastructure for usin
 g the dataset. The fifth contribution is our part in the design and implem
 entation of SpeechBrain\, an open-source software toolkit for speech proce
 ssing. Finally\, using some of the tools and techniques developed earlier 
 in the thesis\, we propose a simplified and unified approach to voice cont
 rol in which the entire traditional pipeline\, composed of an automatic sp
 eech recognition subsystem\, a natural language understanding subsystem\, 
 and human-programmed control logic\, is subsumed within a single deep neur
 al network.\n
DTSTART:20230515T170000Z
DTEND:20230515T190000Z
LOCATION:\, Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\
 , H3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Loren Lugosch - Deep Neural Networks for Voice Contr
 ol
URL:/ece/channels/event/phd-defence-loren-lugosch-deep
 -neural-networks-voice-control-348248
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