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.
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. 7. The method of claim 6 , further comprising utilizing back-propagation to further refine the weights of the connections between the hidden stochastic layers.