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Deep Belief Networks for Phone Recongition

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If you have a question about this talk, please contact Shakir Mohamed.

The current state-of-the-art for acoustic models are Discriminatively trained Hidden Markov Models. There are proposals to use different types of model to improve upon the current state-of-the-art. One model is the Deep Belief Network that can produce a rich distributed representation of speech data. We describe Restricted Boltzmann Machines, how they are composed into a Deep Belief Network, and the application of a Deep Belief Network to phone recognition. If we have time we will touch on another deep structured acoustic model, the deep hidden conditional random field.

This is outlined in the paper http://www.cs.toronto.edu/~gdahl/papers/dbnPhoneRec.pdf

This talk is part of the Machine Learning Reading Group @ CUED series.

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