8. A computer-implemented system comprising: a processor; and a memory that comprises a plurality of components that are executable by the processor, the components comprising: a receiver component that receives a pretrained deep belief network (DBN), wherein the DBN comprises a plurality of hidden layers, weights between the hidden layers, a linear conditional random field (CRF) that comprises output units that each represent possible output states, transition probabilities between output units, and language model scores, the transition probabilities representative of probabilities of transitioning between output states represented by the output units, each output state being one of a phone or senone; and a trainer component that jointly optimizes weights of the pretrained DBN, the transition probabilities of the pretrained DBN, and language model scores of the pretrained DBN based upon a set of training data.
14. The system of claim 8 , wherein the trainer component utilizes back-propagation to jointly optimize the weights between the hidden layers, the transition probabilities, and the language model scores.