ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks

• URL: http://arxiv.org/abs/2511.10090v1
• Date: Fri, 14 Nov 2025 01:31:32 GMT
• Title: ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks
• Authors: Haroun Elleuch, Youssef Saidi, Salima Mdhaffar, Yannick Estève, Fethi Bougares,
• Abstract summary: This paper describes Elyadata & LIA's joint submission to the NADI multi-dialectal Arabic Speech Processing 2025.<n>Our submission ranked first for the ADI subtask and second for the multi-dialectal Arabic ASR subtask among all participants.
• Score: 10.679081563761793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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