This work makes an investigation into speaker verification (SV) from the view of practical systems. Limited data SV is preferred in order to have user comfort and effective decision delivery for regular usage. However, reduction in speech data affects the SV performance that becomes a concern for field deployment. In this work, varied data conditions for SV are explored, and sufficient train with limited test data is presented as a preferable anatomy for practical systems. Different explorations are made from the perspective of improving performance in varied data conditions. These explorations include vocal tract constriction feature to include speaker-specific acoustic–phonetic information, different attributes of voice source features that carry alternative/complementary information from that carried by conventional mel-frequency cepstral coefficient features. Further, kernel discriminant analysis is performed at the back end of i-vector-based speaker modeling for channel/session compensation that is found to work well for varied data conditions. Finally, a framework is proposed in combination with the stated explorations to have a better speaker characterization, which is more effective in case of sufficient train and limited test data scenario. The proposed framework achieves significant improvement in performance [equal error rate (EER): 11.20%, detection cost function (DCF): 0.1990], compared to the baseline (EER: 22.31%, DCF: 0.4128) for sufficient train with 2-s test segment case, showing scope toward application-oriented systems. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.