Track Chair: Assoc. Prof. Samir Rustamov, ADA University, Azerbaijan

The world's linguistic diversity is vast, yet the majority of advances in natural language processing and speech technology have been concentrated on a handful of high-resource languages. Low-resource languages — including endangered, indigenous, and regional languages — remain significantly underserved, facing critical gaps in data availability, computational tools, and research attention. As large language models and speech systems continue to reshape human-computer interaction, ensuring equitable access and inclusive development across all languages has become an urgent research imperative. This track focuses on the unique challenges and opportunities in building robust NLP and speech technologies for low-resource settings, bringing together researchers working on data augmentation, cross-lingual transfer, multilingual modeling, speech recognition, and language preservation to advance inclusive and accessible AI for all languages.