For text-independent speaker identification a prominent combination is to use Gaussian Mixture Models (GMM) for classification while relying on Mel-Frequency Cepstral Coefficients (MFCC) as features. To take temporal information into account the time difference of features of adjacent speech frames are appended to the initial features. In this paper we investigate the applicability of spectro-temporal features obtained from Gabor-Filters and present an algorithm for optimizing the possible parameters. Simulation results on a database show that spectro-temporal features achieve higher recognition rates than purely temporal features for clean speech as well as for disturbed speech.