Patent Document ID: 8972253
Application ID: 12882233

Base Claim:
1. A method executed by a processor, the method comprising: receiving a sample at a context-dependent combination of a Deep Belief Network (DBN) and a Hidden Markov Model (HMM), wherein the sample is a spoken utterance outputting, at the DBN, a posterior probability distribution over labeled senones; outputting, at the HMM, transition probabilities between the labeled senones, the transition probabilities based upon the posterior probability distribution over the labeled senones; and decoding the sample based at least in part upon the posterior probability distribution over the labeled senones and the transition probabilities between the labeled senones.

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Claim 6:
6. The method of claim 1 , further comprising: during a training phase for the context-dependent combination of the DBN and the HMM, performing pretraining with respect to the DBN, the DBN comprises a plurality of hidden stochastic layers, and wherein pretraining comprises utilizing an unsupervised algorithm to initialize weights of connections between the hidden stochastic layers.