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.