针对库尔德语方言差异，提升说话人识别准确率。
From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification
- 为每种方言定制策略并跨方言训练
- 跨方言训练使识别性能显著提升
- 适用于多方言语音识别场景
本文研究了在多种库尔德语方言（包括库尔曼吉、索拉尼和哈瓦拉米）之间进行说话人检测的复杂性与挑战。由于这些方言在语音和词汇上存在巨大差异，给说话人识别系统带来特殊难题。本文深入分析了构建能够精确识别跨方言说话人的强健系统的困难，并提出通过先进的机器学习方法、数据增强技术以及建立全面的方言专属语料库来提升系统准确性和可靠性。实验结果表明，针对每种方言的定制化策略结合跨方言训练，能显著提高识别性能。
原文摘要 · Abstract (English)
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.
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