Neural Speaker Diarization via Multilingual Training: Evaluation on Low-Resource Nepali-Hindi Speech
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Computer Science > Sound
arXiv:2606.26144 (cs)
[Submitted on 21 Jun 2026]
Title:Neural Speaker Diarization via Multilingual Training: Evaluation on Low-Resource Nepali-Hindi Speech
View a PDF of the paper titled Neural Speaker Diarization via Multilingual Training: Evaluation on Low-Resource Nepali-Hindi Speech, by Samip Neupane and 3 other authors
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Abstract:Speaker diarization, the task of determining "who spoke when" in a multi-speaker recording, is a critical component in applications such as meeting transcription, accessibility tools, and multilingual information retrieval. While end-to-end neural diarization systems have achieved strong performance for English and other high-resource languages, their effectiveness degrades substantially for underrepresented languages where annotated speech data is scarce.
This paper investigates speaker diarization for low-resource Nepali-Hindi speech through a multilingual training approach, comparing two modern architectures: EEND with encoder-decoder attractors (EEND-EDA) and EEND with Perceiver-based attractors (DiaPer). Both models are trained on a multilingual corpus combining English speech from LibriSpeech, diverse speaker recordings from VoxCeleb, and separately collected Nepali and Hindi audio, a setup designed to reduce language bias and encourage cross-lingual generalization. We evaluate both models across 2-speaker, 3-speaker, 4-speaker, and mixed-speaker scenarios on LibriSpeech, VoxCeleb, and Nepali-Hindi (NeHi) test sets. DiaPer achieves stronger overall performance than EEND-EDA, particularly in more challenging multi-speaker conditions, obtaining DERs of 3.28%, 2.02%, 4.05%, and 4.76% on NeHi 2-speaker, 3-speaker, 4-speaker, and mixed-speaker settings, respectively, compared to 1.50%, 9.68%, 16.17%, and 11.19% for EEND-EDA. These results demonstrate the viability of Perceiver-based end-to-end neural diarization for low-resource multilingual speech processing.
| Comments: | 12 pages, 7 tables |
| Subjects: | Sound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.26144 [cs.SD] |
| (or arXiv:2606.26144v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26144
arXiv-issued DOI via DataCite
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