Multimodal foundation models are increasingly used in speech-first assistants that must interpret spoken queries and produce visually grounded decisions. Yet it remains unclear whether semantically equivalent queries yield consistent judgments across modality (text vs. speech) and language (English vs. Arabic). We intr...
Basel Mousi, Fahim Dalvi, S. Chowdhury et al.· 2 citations
This work introduces MENASpeechBank, a reference speech bank comprising ~18K high-quality utterances from 124 speakers spanning multiple MENA countries, covering English, Modern Standard Arabic, and regional Arabic varieties, and develops a controllable data pipeline that constructs persona profiles enriched with WVS i...
Zien Sheikh Ali, Hunzalah Hassan Bhatti, R. N. Nandi et al.· arXiv.org· 4 citations
Aslema, the system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling and four omni LLMs in a zero-shot setting are presented and it is shown that fine-tuning consistently out-performs zero-shot inference.
Tajwaar Shafiq, Hunzalah Hassan Bhatti, S. Chowdhury et al.· 0 citations
This study reviews multi-turn conversational AI across text-only dialogue, AudioLLMs and speech-native systems, multimodal and omni-modal systems, and tool-augmented agents, and concludes with a research agenda for systems that can remember, revise, ground, speak, listen, act, and adapt across turns, modalities, and cu...
S. Sara, Zien Sheikh Ali, Hunzalah Hassan Bhatti et al.· 3 citations
An overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation is presented, covering spoken visual question answering and image-grounded hallucination detection in English and Modern Standard Arabic, and CRAI-Bench, evaluating the cultural accuracy of text-to-image generation.
This work evaluates four omni LLMs in a zero-shot setting and shows that fine-tuning consistently outperforms zero-shot inference, and explores synthetic data augmentation by using an LLM to generate culturally grounded Tunisian Derja utterances, followed by voice cloning to generate synthetic speech.
Tajwaar Shafiq, Hunzalah Hassan Bhatti, Firoj Alam et al.· 1 citation· ⚡1
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