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Some new approaches to speech recognition and language modelling

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In the first part of the talk, I will review some of the research at the University of Toronto on the use of deep belief nets for speech recognition. Then I will describe some very recent work that uses a rather different approach to learn acoustic events that have precise times and amplitudes. Finally I will briefly describe a much better way of optimizing recurrent neural networks. When applied to a novel type of recurrent neural net, this new optimizer learns extremely good models of character sequences. This is joint work with George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, James Martens and Ilya Sutskever.

This talk is part of the Machine Intelligence Lab Seminar series.

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