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We present the Factorial Hidden Restricted Boltzmann Machine (FHRBM) for robust speech recognition. Speech and noise are modeled as independent RBMs, and the interaction between them is explicitly modeled to capture how speech and noise combine to generate observed noisy speech features. In contrast with RBMs, where the bottom layer of random variables is observed, inference in the FHRBM is intractable, scaling exponentially with the number of hidden units. We introduce variational algorithmsdoi:10.1109/icassp.2012.6288869 dblp:conf/icassp/RennieFD12 fatcat:cbfbcdys55eghi3rxut63z5pdy