# Automatic Speech Recognition Biases in Newcastle English: an Error Analysis

arXiv: https://arxiv.org/abs/2506.16558 · HTML: https://mlsanity.com/paper/2506.16558/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://mlsanity.com/paper/2303.18110/) (2023)
- [CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions](https://mlsanity.com/paper/2408.16589/) (2024)
- [Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions](https://mlsanity.com/paper/2111.09983/) (2021)
- [Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review](https://mlsanity.com/paper/2211.09511/) (2022)
- [The Sociolinguistic Foundations of Language Modeling](https://mlsanity.com/paper/2407.09241/) (2024)

## Built on by (most cited first, 6 of 6)

- [WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing](https://mlsanity.com/paper/2509.18004/) (2025)
- [Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity](https://mlsanity.com/paper/2601.18641/) (2026)
- [Demographic and Linguistic Bias Evaluation in Omnimodal Language Models](https://mlsanity.com/paper/2604.10014/) (2026)
- [Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models](https://mlsanity.com/paper/2606.11639/) (2026)
- [NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2609.27086/) (2026)
- [Fairness Beyond a Single Run: Training-Seed Variability in Speech LLM Adaptation](https://mlsanity.com/paper/2609.38976/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems struggle with regional dialects due to biased training which favours mainstream varieties. While previous research has identified racial, age, and gender biases in ASR, regional bias remains underexamined. This study investigates ASR performance on Newcastle English, a well-documented regional dialect known to be challenging for ASR. A two-stage analysis was conducted: first, a manual error analysis on a subsample identified key phonological, lexical, and morphosyntactic errors behind ASR misrecognitions; second, a case study focused on the systematic analysis of ASR recognition of the regional pronouns ``yous'' and ``wor''. Results show that ASR errors directly correlate with regional dialectal features, while social factors play a lesser role in ASR mismatches. We advocate for greater dialectal diversity in ASR training data and highlight the value of sociolinguistic analysis in diagnosing and addressing regional biases.