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

arXiv:2609.01737 · cs.CL · Submitted 2026-09-01 · Read on arXiv

cs.CL

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/subedibiraj/speakpay

Terminology

• Adaptation — 23× in this paper · explained in 1 episode
• Fine-Tuning — 13× in this paper · explained in 5 episodes
• Domain Adaptation — 12× in this paper · explained in 5 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Hallucination — 6× in this paper · explained in 4 episodes
• LoRA Fine-Tuning — 5× in this paper · explained in 3 episodes
• LoRA Adaptation — 4× in this paper · explained in 2 episodes
• Low-Resource Languages — 3× in this paper · explained in 4 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Acoustic Robustness — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Automatic Speech Recognition (ASR) — 1× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Character Error Rate — 1× in this paper
• Error Taxonomy — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Nepali Automatic Speech Recognition — 1× in this paper
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• A Theoretical Formulation of Many-body Message Passing Neural Networks
• LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation

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