arXiv cs.CL· Stephen Fernandes, Narayan Choudhary·· 3 天前AI 评分24
Anuvani-Diarization：覆盖 22 种语言的零样本说话人分离与标注规范研究
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages
AI 导读
研究团队在 IndicDiarBench 上系统评测了印度 22 种表列语言的说话人分离，并提出 Anuvani-Diarization，将自监督端到端分割模型通过不确定性感知的 silver 监督适配到印度对话场景。
正文 · 原文
Abstract:India's 22 scheduled languages are spoken by over a billion people in conversations that mix languages, often within one turn, overlap freely, and take place over telephones, in meetings, and in media. Knowing who spoke when, the task of speaker diarization, is essential to speech applications for such conversation: voice agents conversing with several people at once, speaker attributed transcription of meetings and calls, contact center analytics, and media monitoring. We present a systematic study of speaker diarization across all 22 languages on IndicDiarBench and introduce Anuvani-Diarization, which adapts a self-supervised end-to-end segmentation model to Indian conversation with uncertainty aware silver supervision. Evaluated zero shot alongside nine recent diarization systems, it reaches a duration weighted diarization error rate (DER) of 16.78% on all 1,164 recordings, the lowest of all systems we ran and second only to the best published result (16.0%), and leads the three strongest rivals in 14 of 22 languages. It matches its parent on six international benchmarks. We also show that the benchmark's utterance level annotation convention is a hidden variable worth 1.9 to 4.1 DER points, depending on the system, and that a convention learned from other Indian transcriptions does not transfer.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.20327 [cs.CL] |
| (or arXiv:2610.20327v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20327 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Stephen Fernandes [view email]
[v1]
Wed, 14 Oct 2026 15:12:20 UTC (302 KB)
来源：arXiv cs.CL · arxiv.org