In this paper, we present the system that THUEE submitted to NIST 2019 Speaker Recognition Evaluation CTS Challenge (SRE19). Similar to the previous SREs, domain mismatches, such as cross-lingual and cross-channel between the training sets and evaluation sets, remain the major challenges in this evaluation. To improve the robustness of our systems, we develop deeper and wider x-vector architectures. Besides, we use novel speaker discriminative embedding systems, hybrid multi-task learning architectures combined with phonetic information. To deal with domain mismatches, we follow a heuristic search scheme to select the best back-end strategy based on limited development corpus. An extended and factorized TDNN achieves the best single-system results on SRE18 DEV and SRE19 EVAL sets. The final system is a fusion of six subsystems, which yields EER 2.81% and minimum cost 0.262 on the SRE19 EVAL set.