A method comprising the following computer-executed acts: accessing a deep belief network (204, 400), DBN, retained in a computer-readable medium, wherein the DBN (204, 400) comprises: a plurality of stacked hidden layers, each hidden layer comprising a respective plurality of stochastic units, each stochastic unit in each layer connected to stochastic units in adjacent hidden layer of the DBN by way of connections, the connections assigned weights learned during a pretraining procedure; and a linear-chain conditional random field, CRF, at the top-most hidden layer of the DBN (204, 400), the CRF comprising: the top-most layer hidden layer of the DBN comprising an associated plurality of stochastic units; and a plurality of output units that are representative of outputs states, each state in the output states being part of a phone or a senone, the associated plurality of stochastic units connected to the plurality of output units by way of second connections, the second connections having weights learned during the pretraining procedure, wherein the output units have transition probabilities corresponding thereto that are indicative of probabilities of transitioning between output states represented by the output units; and jointly optimizing the weights assigned to the connections, the weights assigned to the second connections, the transition probabilities, and phone or senone language model scores of the DBN (204, 400) combined with the transition probabilities, based upon training data. accessing a deep belief network (204, 400), DBN, retained in a computer-readable medium, wherein the DBN (204, 400) comprises: a plurality of stacked hidden layers, each hidden layer comprising a respective plurality of stochastic units, each stochastic unit in each layer connected to stochastic units in adjacent hidden layer of the DBN by way of connections, the connections assigned weights learned during a pretraining procedure; and a linear-chain conditional random field, CRF, at the top-most hidden layer of the DBN (204, 400), the CRF comprising: the top-most layer hidden layer of the DBN comprising an associated plurality of stochastic units; and a plurality of output units that are representative of outputs states, each state in the output states being part of a phone or a senone, the associated plurality of stochastic units connected to the plurality of output units by way of second connections, the second connections having weights learned during the pretraining procedure, wherein the output units have transition probabilities corresponding thereto that are indicative of probabilities of transitioning between output states represented by the output units; and a plurality of stacked hidden layers, each hidden layer comprising a respective plurality of stochastic units, each stochastic unit in each layer connected to stochastic units in adjacent hidden layer of the DBN by way of connections, the connections assigned weights learned during a pretraining procedure; and a linear-chain conditional random field, CRF, at the top-most hidden layer of the DBN (204, 400), the CRF comprising: the top-most layer hidden layer of the DBN comprising an associated plurality of stochastic units; and a plurality of output units that are representative of outputs states, each state in the output states being part of a phone or a senone, the associated plurality of stochastic units connected to the plurality of output units by way of second connections, the second connections having weights learned during the pretraining procedure, wherein the output units have transition probabilities corresponding thereto that are indicative of probabilities of transitioning between output states represented by the output units; and the top-most layer hidden layer of the DBN comprising an associated plurality of stochastic units; and a plurality of output units that are representative of outputs states, each state in the output states being part of a phone or a senone, the associated plurality of stochastic units connected to the plurality of output units by way of second connections, the second connections having weights learned during the pretraining procedure, wherein the output units have transition probabilities corresponding thereto that are indicative of probabilities of transitioning between output states represented by the output units; jointly optimizing the weights assigned to the connections, the weights assigned to the second connections, the transition probabilities, and phone or senone language model scores of the DBN (204, 400) combined with the transition probabilities, based upon training data.