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 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
The proliferation of AI systems across various applications is driving the need for more inclusive and robust multilingual capabilities, especially for underrepresented languages.
Improving AI performance for low-resource languages expands access to advanced technologies, fosters digital inclusion, and unlocks new markets/user bases for AI applications.
The ability to accurately process and understand speech in low-resource languages like Nepali-Hindi significantly broadens the utility and reach of AI-powered tools such as transcription services and virtual assistants.
- · AI developers targeting underserved markets
- · Populations speaking low-resource languages
- · Multilingual information retrieval systems
- · Monolingual AI solutions
- · Data scarcity as a barrier for AI deployment
Improved speaker diarization for Nepali-Hindi and similar low-resource languages, enabling better use of AI tools.
Increased adoption of AI services in regions and populations previously excluded due to language barriers.
New economic opportunities and digital transformation in 'underrepresented' linguistic communities.
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