Fine-Tuning ECAPA-TDNN For Turkish Speaker Verification
Year: 2024
Type: conference-paper
Abstract: Compared 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.
Authors: Selim Can Demirtaş, Cemal Hanilçi
Institution: Bursa Technical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403448262