16. A computing device comprising a computer-readable medium, the computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform acts comprising: greedily learning weights between hidden layers of a deep belief network (DBN) that is configured for employment in an automatic speech recognition (ASR) system, wherein the DBN is temporally parameter-tied and an uppermost layer in the DBN is a linear-chain conditional random field (CRF), the linear chain CRF comprises a plurality of output units that are representative of respective output states, each output state being one of a phone or senone; providing training data to the DBN to optimize a log of conditional probabilities of output sequences of the DBN, an output sequence comprising a sequence of output states represented by the output units; and jointly optimizing the weights between the DBN, transition probabilities between the output units in the CRF, and language model scores in the DBN based upon the log of the conditional probabilities of output sequences produced by the DBN.
16. A computing device comprising a computer-readable medium, the computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform acts comprising: greedily learning weights between hidden layers of a deep belief network (DBN) that is configured for employment in an automatic speech recognition (ASR) system, wherein the DBN is temporally parameter-tied and an uppermost layer in the DBN is a linear-chain conditional random field (CRF), the linear chain CRF comprises a plurality of output units that are representative of respective output states, each output state being one of a phone or senone; providing training data to the DBN to optimize a log of conditional probabilities of output sequences of the DBN, an output sequence comprising a sequence of output states represented by the output units; and jointly optimizing the weights between the DBN, transition probabilities between the output units in the CRF, and language model scores in the DBN based upon the log of the conditional probabilities of output sequences produced by the DBN. 20. The computing device of claim 16 , wherein back-propagation is used to jointly optimize the weights, the transition probabilities, and the language model scores of the DBN.