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

Speaker Verification Across Ages: Investigating Deep Speaker Embedding Sensitivity to Age Mismatch in Enrollment and Test Speech