1. A speech recognition apparatus based on a deep-neural-network (DNN) sound model, comprising: a memory; and a processor configured to execute a program stored in the memory, wherein, as the program is executed, the processor: generates sound-model state sets corresponding to a plurality of pieces of set training speech data included in multi-set training speech data, generates a multi-set state cluster from the sound-model state sets, generating the multi-set state cluster comprising: collecting the sound model states; calculating respective state log likelihoods of the sound models states; and generating the multi-set state cluster by merging a first sound model state set with a second model state set using the state log likelihoods, learns a DNN structured parameter by setting the multi-set training speech data as an input node of the DNN, setting the multi-set state cluster as an output node of the DNN, and disconnecting the output node from a state cluster of the DNN not related to the input node, and when a user's speech and characteristic information thereof are received via a user interface, recognizes the user's speech on the basis of the learned DNN structured parameter by setting a sound-model state set corresponding to the characteristic information of the user's speech as an output node.