ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks
Haroun Elleuch, Youssef Saidi, Salima Mdhaffar, Yannick Estève, Fethi Bougares · 2025
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 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 38.54% and 14.53%, respectively, on the test set.Our results demonstrate the effectiveness of large pretrained speech models with targeted fine-tuning for Arabic speech processing.