Speaker identification systems are deployed in diverse environments, often different from the lab conditions on which they are trained and tested. In this paper, first, we show the problem of generalization using fixed thresholds computed using the equal error rate metric. Secondly, we introduce a novel and generalizable speaker-specific thresholding technique for robust imposter identification in unseen speaker identification. We propose a speaker-specific adaptive threshold, which can be computed using the enrollment audio samples, for identifying imposters in unseen speaker identification. Furthermore, we show the efficacy of the proposed technique on VoxCeleb1, VCTK and the FFSVC 2022 datasets, beating the baseline fixed thresholding by up to 25%. Finally, we exhibit that the proposed algorithm is also generalizable, demonstrating its performance on ResNet50, ECAPA-TDNN and RawNet3 speaker encoders.

翻译：说话人识别系统部署在多样化的环境中，这些环境往往与训练和测试时的实验室条件不同。本文首先展示了使用等错误率指标计算的固定阈值存在的泛化问题。其次，我们提出了一种新颖且可泛化的说话人特定阈值技术，用于在未知说话人识别中实现稳健的冒名者检测。我们提出了一种说话人自适应的阈值，可利用注册语音样本计算得出，用于识别未知说话人场景中的冒名者。此外，我们在VoxCeleb1、VCTK和FFSVC 2022数据集上展示了所提技术的有效性，相比基线固定阈值方法性能提升高达25%。最后，我们证明了所提算法具有良好的泛化能力，并在ResNet50、ECAPA-TDNN和RawNet3说话人编码器上展现了其性能表现。