The CL-UZH team submitted one system each for the fixed and open conditions of the NIST SRE 2024 challenge. For the closed-set condition, results for the audio-only trials were achieved using the X-vector system developed with Kaldi. For the audio-visual results we used only models developed for the visual modality. Two sets of results were submitted for the open-set and closed-set conditions, one based on a pretrained model using the VoxBlink2 and VoxCeleb2 datasets. An Xvector-based model was trained from scratch using the CTS superset dataset for the closed set. In addition to the submission of the results of the SRE24 evaluation to the competition website, we talked about the performance of the proposed systems on the SRE24 evaluation in this report.

翻译：CL-UZH团队针对NIST SRE 2024挑战赛的固定条件与开放条件各提交了一套系统。在封闭集条件下，纯音频测试结果采用基于Kaldi开发的X-vector系统实现。视听融合结果则仅使用为视觉模态开发的模型。针对开放集与封闭集条件共提交了两组结果：其中一组基于使用VoxBlink2与VoxCeleb2数据集预训练的模型；针对封闭集条件，另使用CTS超集数据集从头训练了基于Xvector的模型。除向竞赛网站提交SRE24评测结果外，本报告亦探讨了所提系统在SRE24评测中的性能表现。