In this paper a novel set of features with a promising ability to identify speakers is presented. These features are based on the harmonic phase of the speech signal and have been previously used successfully in an ASR task. Using the SI-284 subset of the WSJ database, a GMM has been trained for each of the 283 speakers and several speaker identification experiments have been performed, with a high level of success. The feature extraction method and the performed experiments are described. The results show that the features present excellent identification performance, very close to the performance of the MFCC parameters.