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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.04629 |
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| _version_ | 1866912626674499584 |
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| author | Abdullah, Abdulhady Abas Badawi, Soran Abdullah, Dana A. Hamad, Dana Rasul |
| author_facet | Abdullah, Abdulhady Abas Badawi, Soran Abdullah, Dana A. Hamad, Dana Rasul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification Abdullah, Abdulhady Abas Badawi, Soran Abdullah, Dana A. Hamad, Dana Rasul Audio and Speech Processing Artificial Intelligence Computation and Language 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. |
| title | From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.04629 |