Orientador: Prof. Dr. Francisco J. Fraga / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2014. / Most speaker verifcation systems are based on Gaussian mixture models and more recently on the so-called i-vector. These two methods are affected in mismatched testtrain conditions, which might be caused by vocal-efort variability, different speakingstyles or channel efects. In this work, we compared the impact of speech rate variation and room reverberation on both methods. We found that performance degradation due to variation on speech rate can be mitigated by adding fast speech samples into the training set, which decreased equal error rates for Gaussian mixture models and i-vector, respectively. Regarding reverberation, we investigated the achievements of both methods when three diferent reverberation compensation techniques are applied in order to overcome performance degradation. The results showed that having reverberant background models separated by diferent levels of reverberation can bene t both methods, with the i-vector providing the best performance in that scenario. Finally, the performance of two auditory-inspired features, mel-frequency cepstral coe ficients and the so-called modulation spectrum features, are compared in presence of room reverberation. For the speaker verifcation system considered in this work, modulation spectrum features are equally afected by reverberation time and have their performance degraded as the level of reverberation increases.