  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.
