  This paper presents the SJTU system for both text-dependent and
text-independent tasks in short-duration speaker verification (SdSV) challenge
2021. In this challenge, we explored different strong embedding extractors to
extract robust speaker embedding. For text-independent task, language-dependent
adaptive snorm is explored to improve the system performance under the
cross-lingual verification condition. For text-dependent task, we mainly focus
on the in-domain fine-tuning strategies based on the model pre-trained on
large-scale out-of-domain data. In order to improve the distinction between
different speakers uttering the same phrase, we proposed several novel
phrase-aware fine-tuning strategies and phrase-aware neural PLDA. With such
strategies, the system performance is further improved. Finally, we fused the
scores of different systems, and our fusion systems achieved 0.0473 in Task1
(rank 3) and 0.0581 in Task2 (rank 8) on the primary evaluation metric.
