The complexity and difficulties of Kurdish speaker detection among its several dialects are investigated in this work. Because of its great phonetic and lexical differences, Kurdish with several dialects including Kurmanji, Sorani, and Hawrami offers special challenges for speaker recognition systems. The main difficulties in building a strong speaker identification system capable of precisely identifying speakers across several dialects are investigated in this work. To raise the accuracy and dependability of these systems, it also suggests solutions like sophisticated machine learning approaches, data augmentation tactics, and the building of thorough dialect-specific corpus. The results show that customized strategies for every dialect together with cross-dialect training greatly enhance recognition performance.
View on arXiv@article{abdullah2025_2505.04629,
title={ From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification },
author={ Abdulhady Abas Abdullah and Soran Badawi and Dana A. Abdullah and Dana Rasul Hamad and Hanan Abdulrahman Taher and Sabat Salih Muhamad and Aram Mahmood Ahmed and Bryar A. Hassan and Sirwan Abdolwahed Aula and Tarik A. Rashid },
journal={arXiv preprint arXiv:2505.04629},
year={ 2025 }
}