Effect of long-term ageing on i-vector speaker verification
Finnian Kelly, Rahim Saeidi, Naomi Harte, David A. van Leeuwen · 2014
Assessing the impact of ageing on biometric systems is an important challenge. In this paper, an i-vector speaker verifi-cation framework is used to evaluate the impact of long-term ageing on state-of-the-art speaker verification. Using the Trin-ity College Dublin Speaker Ageing (TCDSA) database, it is ob-served that the performance of the i-vector system, in terms of both discrimination and calibration, degrades progressively as the absolute age difference between training and testing sam-ples increases. In the case of male speakers, the equal error rate (EER) increases from 4.61 % at an ageing difference of 0–1 years to 32.74 % at an age difference of 51–60 years. The performance of a Gaussian Mixture Model- Universal Back-ground Model (GMM-UBM) system is presented for compari-son. It is shown that while the i-vector system outperforms the GMM-UBM system, as absolute age difference increases, the performance of both degrades at a similar rate. It is concluded that long-term ageing variability is distinct from everyday inter-session variability, and therefore must be dealt with via dedi-cated compensation strategies.