10. A histogram normalization method, comprising: extracting a speech feature vector from a received speech signal by dividing the received speech signal into frame units; applying a fast Fourier transform (FFT) to the divided speech signal; frequency domain filtering the transformed speech signal; logarithmically compressing the frequency domain filtered speech signal so as to reduce a dynamic range thereof; performing a discrete cosine transform (DCT) to eliminate a correlation between signals in each band of the speech signal; performing a mean variance normalization (MVN) so that the signals in each band have a zero mean and a unit variance; and normalizing a histogram for the speech feature vector using a backward histogram so that a statistical feature of a feature vector of a noisy speech signal is similar to a model learning vector of a clean speech signal, based on the mean variance normalization.
10. A histogram normalization method, comprising: extracting a speech feature vector from a received speech signal by dividing the received speech signal into frame units; applying a fast Fourier transform (FFT) to the divided speech signal; frequency domain filtering the transformed speech signal; logarithmically compressing the frequency domain filtered speech signal so as to reduce a dynamic range thereof; performing a discrete cosine transform (DCT) to eliminate a correlation between signals in each band of the speech signal; performing a mean variance normalization (MVN) so that the signals in each band have a zero mean and a unit variance; and normalizing a histogram for the speech feature vector using a backward histogram so that a statistical feature of a feature vector of a noisy speech signal is similar to a model learning vector of a clean speech signal, based on the mean variance normalization. 12. The histogram normalization method of claim 10 , further comprising: calculating a probability distribution function corresponding to each band; and normalizing the histogram for the speech feature vector using the backward cumulative distribution function which cumulates the probability distribution function in an order from a greatest to a smallest value.