Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

NeurIPS 2010 George DahlMarc'Aurelio RanzatoAbdel-Rahman MohamedGeoffrey E. Hinton

Straightforward application of Deep Belief Nets (DBNs) to acoustic modeling produces a rich distributed representation of speech data that is useful for recognition and yields impressive results on the speaker-independent TIMIT phone recognition task. However, the first-layer Gaussian-Bernoulli Restricted Boltzmann Machine (GRBM) has an important limitation, shared with mixtures of diagonal-covariance Gaussians: GRBMs treat different components of the acoustic input vector as conditionally independent given the hidden state... (read more)

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