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New neural diarization model excels on low-resource Nepali-Hindi speech

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new approach to speaker diarization, the process of identifying who spoke when in an audio recording, specifically for low-resource languages like Nepali-Hindi. They trained two neural network architectures, EEND-EDA and DiaPer, on a multilingual dataset that included English, diverse speaker recordings, and newly collected Nepali and Hindi audio. The DiaPer model, utilizing Perceiver-based attractors, demonstrated superior performance, achieving significantly lower diarization error rates (DERs) on Nepali-Hindi test sets compared to the EEND-EDA model, particularly in challenging multi-speaker scenarios. AI

IMPACT This research advances speaker diarization capabilities for underrepresented languages, potentially improving accessibility and information retrieval tools for diverse linguistic communities.

RANK_REASON Academic paper detailing a new model architecture and evaluation on specific datasets. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New neural diarization model excels on low-resource Nepali-Hindi speech

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Academic paper detailing a new model architecture and evaluation on specific datasets. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Samip Neupane, Sandesh Pokhrel, Sandesh Pyakurel, Basanta Joshi ·

    Neural Speaker Diarization via Multilingual Training: Evaluation on Low-Resource Nepali-Hindi Speech

    arXiv:2606.26144v1 Announce Type: cross 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 …

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