12. A speech recognition method, comprising: extracting acoustic model-state level information corresponding to an input speech signal from a training data model set using at least one of a first feature vector based on a gammatone filterbank signal analysis algorithm and a second feature vector based on a bottleneck algorithm; and providing a result of recognizing the input speech signal based on the extracted acoustic model-state level information, wherein the extracting of the acoustic model-state level information includes obtaining the acoustic model-state level information by applying at least one of the first feature vector and the second feature vector to a Gaussian mixture model-hidden Markov model (GMM-HMM) technology based training data model set, wherein the first feature vector is determined based on an intra-frame feature value associated with a feature of a rapid change in a spectrum of the input speech signal, a static feature value associated with a static feature of the input speech signal, and an inter-frame feature value associated with a dynamic feature of the input speech signal based on a lapse of a period of time, and the second feature vector is obtained by directly learning an equation needed for extracting a feature from the training data model set based on a deep neural network (DNN) algorithm.
12. A speech recognition method, comprising: extracting acoustic model-state level information corresponding to an input speech signal from a training data model set using at least one of a first feature vector based on a gammatone filterbank signal analysis algorithm and a second feature vector based on a bottleneck algorithm; and providing a result of recognizing the input speech signal based on the extracted acoustic model-state level information, wherein the extracting of the acoustic model-state level information includes obtaining the acoustic model-state level information by applying at least one of the first feature vector and the second feature vector to a Gaussian mixture model-hidden Markov model (GMM-HMM) technology based training data model set, wherein the first feature vector is determined based on an intra-frame feature value associated with a feature of a rapid change in a spectrum of the input speech signal, a static feature value associated with a static feature of the input speech signal, and an inter-frame feature value associated with a dynamic feature of the input speech signal based on a lapse of a period of time, and the second feature vector is obtained by directly learning an equation needed for extracting a feature from the training data model set based on a deep neural network (DNN) algorithm. 15. The method of claim 12 , wherein the extracting of the acoustic model-state level information comprises: extracting the acoustic model-state level information additionally using a third feature vector comprising at least one of a spectral entropy based additional feature, a harmonic component ratio based additional feature, and a pitch information based additional feature.