1. A pre-training apparatus for speech recognition, the pre-training apparatus comprising: a memory and at least one processor coupled to the memory; a set of units configured to pre-train a deep neural network, the set of units comprising: an input unit configured to receive speech data; a model generation unit configured to initialize a connection weight of a deep neural network, based on the speech data; and an output unit configured to output information about the connection weight; wherein in order for a state of a phoneme unit corresponding to the speech data to be output, the model generation unit trains the connection weight by piling a plurality of hidden layers according to a determined structure of the deep neural network, applies an output layer to a certain layer between the plurality of hidden layers to correct the trained connection weight in each of the plurality of hidden layers, thereby initializing the connection weight, wherein when generating a structure of deep neural network by piling a plurality of hidden layers, the model generation unit applies the output layer to one hidden layer to correct a connection weight of the one hidden layer, removes the output layer, piles another hidden layer, applies the output layer to the other hidden layer to correct a connection weight of the other hidden layer, sequentially performs the application and correction on a next hidden layer to a last hidden layer to correct a connection weight of each of the hidden layers subsequent to the other hidden layer, thereby initializing the connection weight.