ASR systems optimised for Word Error Rate (WER) often miss named entities and filled pauses in accented conversational English, both critical for language-learning feedback.
We present a three-stage pipeline for speakers from India, Indonesia, and Latin America:
- heuristic SQL filters curating entity-rich training data at 2.8x the entity density of random sampling,
- regional LoRA adapters fine-tuned on Qwen2.5-Omni-3B producing both verbatim and corrected transcripts in a single forward pass,
- a six-category error taxonomy validated by an LLM-based judge (83.8% agreement, 210 human-labelled samples).
The pipeline achieves 80-85% entity recall (up from 53-55%), 76-86% filler recall (up from <5%), and 6-10% WER across 6k test utterances, outperforming Whisper and a commercial ASR on entity recall while matching a zero-shot 30B model with 10x fewer parameters.
Paired bootstrap tests confirm that curation alone accounts for 2.8-4.2 pp of entity recall gain (p<0.0001).