Recently, the set of spectral parameters of every speech frame that result from filtering the frequency sequence of mel-scaled filter-bank energies with a simple first-order high-pass FIR filter have proved to be an efficient speech representation in terms of both speech recognition rate and computational load. In this paper, we apply the same technique to speaker recognition. Frequency filtering approximately equalizes the cepstrum variance, enhancing the oscillations of the spectral envelope curve that are most effective for discriminating between speakers. In this way, even better speaker identification results than using conventional mel-cepstrum were observed in continuous observation Gaussian density HMM, especially in noisy conditions.