From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification

TL;DR

This paper investigates the challenges of Kurdish speaker verification across multiple dialects and proposes tailored machine learning solutions to improve accuracy and robustness in dialect-rich environments.

Contribution

It introduces dialect-specific strategies and cross-dialect training methods to enhance speaker recognition performance in linguistically diverse Kurdish dialects.

Findings

Customized dialect strategies improve recognition accuracy.

Cross-dialect training enhances system robustness.

Data augmentation contributes to better performance.

Abstract

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.