Demirtaş, Selim CanHanilçi, Cemal2026-02-082026-02-0820249798331531492https://doi.org/10.1109/IDAP64064.2024.10710963https://hdl.handle.net/20.500.12885/53108th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 2024-09-21 through 2024-09-22 -- Malatya -- 203423Compared to Turkish speech databases, English speech databases are significantly larger, featuring many more speakers. This creates a trade-off between data adequacy and language for Turkish ASV systems. This paper explores this trade-off by comparing three different approaches using the state-of-the-art ECAPA-TDNN model: utilizing the pre-trained English ECAPA-TDNN model, training the ECAPA-TDNN model from scratch with the Turkish Common Voice dataset, and fine-tuning the pre-trained English ECAPA-TDNN model with Turkish data. Experimental results reveal that the pre-trained English ECAPA-TDNN model outperforms the model trained from scratch on Turkish data and the fine-tuned model in terms of the equal error rate (EER) criterion. However, the fine-tuning approach demonstrates the best performance according to the minimum detection cost function (min-DCF) metric when security is prioritized over user convenience. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessautomatic speaker verificationfine-tuningConference Object10.1109/IDAP64064.2024.107109632-s2.0-85207867739N/A