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Whisper fine-tuned for Nepali financial voice commands, improving accessibility

Researchers have developed SpeakPay, a voice-first digital wallet designed to assist visually impaired users in Nepal. The core technical contribution is a domain-adaptive fine-tuning approach using LoRA on the Whisper large-v2 model. This fine-tuning, applied to a new dataset of 403 Nepali financial commands called NepFinSpeech-403, significantly reduced the Word Error Rate from 129.95% to 42.58% and improved transaction success rates by approximately 20 times. The study also found that as few as 100 domain-specific utterances could halve the error rate, with performance stabilizing around 300 examples. AI

IMPACT Enhances accessibility for visually impaired users in low-resource regions by improving financial voice command recognition.

RANK_REASON The cluster is based on an academic paper detailing a new dataset and fine-tuning methodology for speech recognition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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Whisper fine-tuned for Nepali financial voice commands, improving accessibility

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The cluster is based on an academic paper detailing a new dataset and fine-tuning methodology for speech recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Biraj Subedi ·

    SpeakPay: Domain-Adaptive LoRA Fine-Tuning of Whisper for Low-Resource Nepali Financial Speech Recognition

    arXiv:2609.01737v1 Announce Type: new Abstract: Mobile payment applications in Nepal are graphically mediated and largely inaccessible to visually impaired users. This paper presents SpeakPay, a voice-first digital wallet, and documents the central technical contribution: a contr…