This report describes the UNISOUND submission for Track1 and Track2 of VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC 2023). We submit the same system on Track 1 and Track 2, which is trained with only VoxCeleb2-dev. Large-scale ResNet and RepVGG architectures are developed for the challenge. We propose a consistency-aware score calibration method, which leverages the stability of audio voiceprints in similarity score by a Consistency Measure Factor (CMF). CMF brings a huge performance boost in this challenge. Our final system is a fusion of six models and achieves the first place in Track 1 and second place in Track 2 of VoxSRC 2023. The minDCF of our submission is 0.0855 and the EER is 1.5880%.

翻译：本报告描述了UNISOUND团队在VoxCeleb说话人识别挑战赛2023（VoxSRC 2023）Track1和Track2中的参赛方案。我们在Track1和Track2中提交了相同的系统，该系统仅使用VoxCeleb2-dev数据集进行训练。针对此次挑战，我们开发了大规模ResNet和RepVGG架构。提出了一种一致性感知分数校准方法，该方法通过一致性度量因子（CMF）利用语音声纹在相似度分数中的稳定性。CMF在此次挑战中带来了巨大的性能提升。最终系统由六个模型融合而成，在VoxSRC 2023的Track1中荣获第一名，在Track2中荣获第二名。我们提交结果的minDCF为0.0855，等错误率（EER）为1.5880%。