ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks
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| Format: | Preprint |
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2025
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| _version_ | 1866908650398810112 |
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| author | Elleuch, Haroun Saidi, Youssef Mdhaffar, Salima Estève, Yannick Bougares, Fethi |
| author_facet | Elleuch, Haroun Saidi, Youssef Mdhaffar, Salima Estève, Yannick Bougares, Fethi |
| contents | This paper describes Elyadata \& LIA's joint submission to the NADI multi-dialectal Arabic Speech Processing 2025. We participated in the Spoken Arabic Dialect Identification (ADI) and multi-dialectal Arabic ASR subtasks. Our submission ranked first for the ADI subtask and second for the multi-dialectal Arabic ASR subtask among all participants. Our ADI system is a fine-tuned Whisper-large-v3 encoder with data augmentation. This system obtained the highest ADI accuracy score of \textbf{79.83\%} on the official test set. For multi-dialectal Arabic ASR, we fine-tuned SeamlessM4T-v2 Large (Egyptian variant) separately for each of the eight considered dialects. Overall, we obtained an average WER and CER of \textbf{38.54\%} and \textbf{14.53\%}, respectively, on the test set. Our results demonstrate the effectiveness of large pre-trained speech models with targeted fine-tuning for Arabic speech processing. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_10090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks Elleuch, Haroun Saidi, Youssef Mdhaffar, Salima Estève, Yannick Bougares, Fethi Computation and Language This paper describes Elyadata \& LIA's joint submission to the NADI multi-dialectal Arabic Speech Processing 2025. We participated in the Spoken Arabic Dialect Identification (ADI) and multi-dialectal Arabic ASR subtasks. Our submission ranked first for the ADI subtask and second for the multi-dialectal Arabic ASR subtask among all participants. Our ADI system is a fine-tuned Whisper-large-v3 encoder with data augmentation. This system obtained the highest ADI accuracy score of \textbf{79.83\%} on the official test set. For multi-dialectal Arabic ASR, we fine-tuned SeamlessM4T-v2 Large (Egyptian variant) separately for each of the eight considered dialects. Overall, we obtained an average WER and CER of \textbf{38.54\%} and \textbf{14.53\%}, respectively, on the test set. Our results demonstrate the effectiveness of large pre-trained speech models with targeted fine-tuning for Arabic speech processing. |
| title | ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.10090 |