This paper presents speaker recognition (SR) systems for the text-independent speaker verification under the cross-lingual (English vs Persian) task (task 2) of the Short-duration Speaker Verification Challenge (SdSVC) 2021.

We present the description of applied ResNet-like and ECAPA-TDNN-like topology design solutions as well as an analysis of multi-session scoring techniques benchmarked on the SdSVC challenge datasets. We overview various modifications of the basic ResNet-like architecture and training strategies, allowing us to obtain the improved quality of speaker verification. Also, we introduce the alpha query expansion-based technique (αQE) to the enrollment embeddings aggregation at test time, which results in a 0.042 minDCF improvement from 0.12 to 0.078 for the ECAPA-TDNN system compared to the embeddings mean. We also propose a trial-level distance-based non-parametric imposter/target detector (KrTC) used to filter out the worst enrollment samples at test time to further improve the performance of the system.