Automatic Speech Recognition Biases in Newcastle English: an Error Analysis
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918129456644096 |
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| author | Serditova, Dana Tang, Kevin Steffens, Jochen |
| author_facet | Serditova, Dana Tang, Kevin Steffens, Jochen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Speech Recognition Biases in Newcastle English: an Error Analysis Serditova, Dana Tang, Kevin Steffens, Jochen Computation and Language Computers and Society Sound Audio and Speech Processing 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. |
| title | Automatic Speech Recognition Biases in Newcastle English: an Error Analysis |
| topic | Computation and Language Computers and Society Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.16558 |