In this paper, we study the impact of the ageing on modern deep speaker embedding based automatic speaker verification (ASV) systems. We have selected two different datasets to examine ageing on the state-of-the-art ECAPA-TDNN system. The first dataset, used for addressing short-term ageing (up to 10 years time difference between enrollment and test) under uncontrolled conditions, is VoxCeleb. The second dataset, used for addressing long-term ageing effect (up to 40 years difference) of Finnish speakers under a more controlled setup, is Longitudinal Corpus of Finnish Spoken in Helsinki (LCFSH). Our study provides new insights into the impact of speaker ageing on modern ASV systems. Specifically, we establish a quantitative measure between ageing and ASV scores. Further, our research indicates that ageing affects female English speakers to a greater degree than male English speakers, while in the case of Finnish, it has a greater impact on male speakers than female speakers.

翻译：本文研究了年龄变化对基于现代深度说话人嵌入的自动说话人确认（ASV）系统的影响。我们选取了两个不同数据集，用于考察最先进的ECAPA-TDNN系统在年龄变化下的表现。第一个数据集是VoxCeleb，用于在非受控条件下研究短期年龄变化（注册与测试之间时间差不超过10年）的影响。第二个数据集是赫尔辛基芬兰语口语纵向语料库（LCFSH），用于在更受控的设置下研究芬兰语说话人的长期年龄变化效应（时间差长达40年）。我们的研究为说话人年龄变化对现代ASV系统的影响提供了新的见解。具体而言，我们建立了年龄变化与ASV分数之间的定量度量。此外，研究表明，年龄变化对女性英语说话人的影响大于男性英语说话人，而在芬兰语场景下，对男性说话人的影响则大于女性说话人。