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Lipi-Ghor: A Large-Scale, Diarized, Multi-Domain Bengali Speech Corpus and a Scalable Pipeline for Building It

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Researchers introduce Lipi-Ghor, a weakly-supervised Bengali speech corpus of 885 hours from 998 YouTube videos across 596 channels and 17 domains, featuring speaker diarization and long-form annotations, along with a replicable pipeline and a human-verified gold evaluation set.

SourcearXiv Computational LinguisticsAuthor: Sanjid Hasan, A H M Fuad, Risalat Labib, Bayazid Hasan Simon
Lipi-Ghor: A Large-Scale, Diarized, Multi-Domain Bengali Speech Corpus and a Scalable Pipeline for Building It
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[Submitted on 26 Oct 2026]

Title:Lipi-Ghor: A Large-Scale, Diarized, Multi-Domain Bengali Speech Corpus and a Scalable Pipeline for Building It

View a PDF of the paper titled Lipi-Ghor: A Large-Scale, Diarized, Multi-Domain Bengali Speech Corpus and a Scalable Pipeline for Building It, by Sanjid Hasan and 3 other authors

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Abstract: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 speaker diarization and long-form audio.

We present Lipi-Ghor, a large-scale weakly-supervised Bengali speech corpus comprising 885 hours of audio sourced from 998 YouTube videos across 596 unique channels, spanning 17 diverse domains including audiobooks, drama, religious sermons, news, and regional dialects. Each recording is annotated in SSTT format (Speaker, Speech, Transcription, Timestamp), which combines automated diarization with Bengali YouTube caption tracks.

To our knowledge, Lipi-Ghor is the first Bengali speech corpus to pair large-scale coverage with speaker-diarized, long-form audio annotations, both of which are absent from existing open Bengali resources. We also present the construction methodology as a replicable pipeline for any language with a sufficient captioned YouTube presence. Targeted channel curation across a handful of rich domains yields corpus scales that were previously out of reach for low-resource languages.

We benchmark four widely-used ASR models under zero-shot and fine-tuned conditions. All four degrade consistently on emotive, dialectal, and long-form content. Because caption targets are noisy, we also release a human-annotated gold evaluation split, verified by 17 volunteer annotators over 30 hours of human-in-the-loop correction and stratified across all 17 domains.

We release our original contributions (video-ID lists, diarization output, pipeline code, and the gold annotations) under CC-BY-4.0, and distribute caption-derived transcripts for research use only; no raw audio is redistributed.

Comments: 12 pages, 7 figures. Published in the Proceedings of the Speech and Audio Language Models Workshop (2nd Edition), pages 190-201, 2026

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2610.36184 [cs.CL]

(or arXiv:2610.36184v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.36184

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sanjid Hasan [view email] [v1] Mon, 26 Oct 2026 19:17:52 UTC (1,199 KB)

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Key points

  • Lipi-Ghor is the first Bengali speech corpus to combine large-scale coverage with speaker-diarized, long-form audio annotations.
  • The corpus comprises 885 hours of audio from 998 YouTube videos across 596 unique channels, spanning 17 domains such as audiobooks, drama, sermons, news, and regional dialects.
  • Each recording is annotated in SSTT format, combining automated diarization with Bengali YouTube captions; a 30-hour human-annotated gold evaluation split stratified across all 17 domains is also released.
  • Benchmarking of four ASR models shows consistent degradation on emotive, dialectal, and long-form content.
  • The construction pipeline is replicable for any language with sufficient captioned YouTube presence; original contributions are released under CC-BY-4.0, while caption-derived transcripts are for research use only and no raw audio is redistributed.

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