Bengali, spoken by over 230 million people, remains severely under-resourced in spoken language research. Existing open Bengali ASR corpora are confined to short, read or crowd-sourced speech and lack...
Lipi-Ghor is a large-scale, weakly-supervised Bengali speech corpus containing 885 hours of audio from 998 YouTube videos across 17 domains, each annotated in SSTT format with speaker diarization and timestamps. It is the first Bengali corpus to combine extensive coverage with speaker-diarized, long-form audio annotations, and the authors provide a replicable pipeline for building similar resources in other languages. The dataset includes a human-annotated gold evaluation split and is released under CC‑BY‑4.0, with raw audio withheld.
By Sanjid Hasan, A H M Fuad, Risalat Labib, Bayazid Hasan Simon
arXiv:2606. 14762v1 Announce Type: cross Abstract: As video content continues to expand across educational platforms, recorded lectures, and live-streamed entertainment, the need for efficient and structured analysis of long-form footage has increased \cite{1}.
By Julian Abelarde, Hugo Garrido-Lestache Belinchon
LF-VideoQA is a new dataset for long‑form, open‑ended question answering over narrative videos, built around theory‑of‑mind questions that require reasoning across scenes averaging 23 minutes. It contains over 6,300 question‑answer‑rubric triples generated automatically from annotated screenplays and validated by humans. The dataset challenges current vision‑language models, showing that even oracle models fall short and that visual evidence is essential for answering these questions.
By Shaden Shaar, Bradon Thymes, Majd Aldaye, Claire Cardie, Bharath Hariharan
arXiv:2511. 05550v3 Announce Type: replace-cross Abstract: Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio.
By Daniel Chenyu Lin, Michael Freeman, John Thickstun
arXiv:2603. 18558v2 Announce Type: replace-cross Abstract: Long-form video question answering requires reasoning over extended temporal contexts, making frame selection a critical bottleneck for multi-modal large language models (MLLMs) bound by finite context windows.
By Dan Ben-Ami, Gabriele Serussi, Kobi Cohen, Chaim Baskin