Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages

Abstract:In this work, we introduce Indic DiarBench, a speaker diarization and ASR benchmark dataset spanning all 22 scheduled languages of India. This corpus comprises approximately 108 hours of natural multi-speaker audio from near-field meetings, far-field recordings, and in-the-wild audios. All annotations are human-corrected with time-aligned speaker attributed transcriptions. The dataset captures conversational nuance prevalent in Indian speech, such as English code-mixing, dialectal variation, and frequent speaker overlap. To establish a baseline for joint ASR and diarization capabilities we evaluate leading systems including commercial speech APIs and multimodal large language models. Indic DiarBench is released as an open-access resource to advance inclusive, multilingual speech technology research for Indian languages.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2607.23808 [cs.CL]

(or arXiv:2607.23808v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.23808 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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