SpeakPay: Domain-Adaptive LoRA Fine-Tuning of Whisper for Low-Resource Nepali Financial Speech Recognition
SpeakPay is a voice-first digital wallet that addresses graphical interface accessibility barriers in Nepal's mobile payment ecosystem. The paper introduces NepFinSpeech-403, a 403-utterance Nepali financial voice command dataset spanning send, load, and balance operations with 237 numerals, and applies LoRA-based domain adaptation to Whisper large-v2. This adaptation cuts Word Error Rate from 129.95% to 42.58% (67.2% relative reduction), raises Devanagari numeral accuracy from 0% to 73.9%, and improves Transaction Success Rate from 1.67% to 33.33% — roughly a 20x gain. As few as 100 domain utterances halve zero-shot WER, and performance plateaus around 300.
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[Submitted on 1 Sep 2026]
Title:SpeakPay: Domain-Adaptive LoRA Fine-Tuning of Whisper for Low-Resource Nepali Financial Speech Recognition
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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 controlled study of domain adaptation for low-resource financial speech recognition. We introduce NepFinSpeech-403, a 403-utterance dataset of Nepali financial voice commands (send, load, and balance operations spanning 237 unique numerals), and fine-tune Whisper large-v2 with LoRA. On the held-out test set, the domain-adapted model reduces Word Error Rate from 129.95% (zero-shot baseline) to 42.58% --- a 67.2% relative reduction --- and improves Devanagari numeral recognition accuracy from 0.0% to 73.9%. We find that word-level metrics understate the practical task-level impact: domain adaptation improves the Transaction Success Rate from 1.67% to 33.33%, a roughly 20x gain. The improvement is consistent at the individual-utterance level (sign test, $p
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