Aslema is a system developed for the NADI 2026 Shared Task 5, which involves intent recognition and slot filling. The team evaluated four omni LLMs in a zero‑shot setting and found that fine‑tuned models consistently outperform zero‑shot inference. They further improved performance by augmenting data with culturally grounded Tunisian Derja utterances generated by an LLM and synthetic speech produced via voice cloning, achieving top‑ranked results on the official test set.
By Tajwaar Shafiq, Hunzalah Hassan Bhatti, Shammur Absar Chowdhury, Firoj Alam
NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification shared task series and the second focused on multidialectal Arabic speech processing. It includes five main tasks—Automatic Speech Recognition, Spoken Dialect Identification, Text-to-Speech, Spoken Language Translation, and Spoken Language Understanding—along with eight subtasks that test realistic scenarios such as low‑bandwidth, mixed dialects, code‑switching, out‑of‑domain, and zero‑shot settings. The event attracted 21 teams from at least 13 countries, with 48 test‑phase submissions and 14 system‑description papers, and the results highlight out‑of‑domain generalization as a major bottleneck while showcasing the strengths of Arabic‑specialized speech models, multimodal dialect identification, and ensemble methods.
By Peter Sullivan, Bashar Talafha, Ahmed Ashraf, Fethi Bougares, Haroun Elleuch, Chiyu Zhang, AbdelRahim Elmadany, Youssef Mohamed, Salima Mdhaffar, Yannick Est\`eve, Mohamed Elhoseiny, Hamzah Luqman, Nizar Habash, Muhammad Abdul-Mageed
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.