[Submitted on 22 Jul 2026]
Title:Beyond WER: Entity and Disfluency Recall in Accented Conversational ASR
View a PDF of the paper titled Beyond WER: Entity and Disfluency Recall in Accented Conversational ASR, by Fiza Husain and 2 other authors
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Abstract: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: (1) heuristic SQL filters curating entity-rich training data at 2.8x the entity density of random sampling, (2) regional LoRA adapters fine-tuned on Qwen2.5-Omni-3B producing both verbatim and corrected transcripts in a single forward pass, and (3) 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
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