arXiv cs.AI· Dana Serditova, Kevin Tang, Jochen Steffens·· 14 小时前AI 评分31
研究：语音 AI 的地区口音会引发社会语言学偏见，Geordie 用户会主动改口音迁就技术
"It Would Make a Geordie Feel at Home": Regional Accent Perceptions and Speech Accommodation in Voice AI
AI 导读
一项针对 Newcastle English（Geordie）使用者的研究发现，语音助手用当地口音时被认为亲切、有归属感但缺乏专业权威，用 SSBE 口音时则被视为更中立、清晰、可信。当语音技术识别失败时，参与者会主动转向更主流的口音、吐字更清晰并放慢语速，形成类似人际交流中的语言迁就。研究认为语音技术会通过促使用户压抑地区口音特征、并以合成语音激活既有社会刻板印象，从而复制口音偏见。
正文 · 原文
Abstract:Automatic speech recognition (ASR) systems often perform less accurately for speakers of regional dialects, but less is known about how users respond to these limitations and how regional voices produced by voice assistants (VAs) are evaluated through a sociolinguistic lens. This study investigates these issues among speakers of Newcastle English (Geordies), examining both linguistic accommodation to voice technologies and perceptions of regional versus mainstream-accented VA voices. Participants interacted with a simulated VA that spoke either Newcastle English or Standard Southern British English (SSBE). They subsequently completed semi-structured interviews about their experiences with the experimental VA and voice technologies, language use, accent attitudes, and place orientation. The findings reveal a two-way process of sociolinguistic bias. The Geordie VA was associated with familiarity, warmth, personality, and local belonging, but also with concerns about professionalism and authority, whereas the SSBE voice was perceived as more neutral, clear, and credible. Participants also reported adapting their speech when voice technologies failed to recognise them, most commonly by shifting to a more mainstream accent, enunciating more clearly, and speaking more slowly. These strategies are similar to accommodation in human-human interaction and reflect users' perceptions of what technologies can recognise, also known as perceived intelligibility pressures. Overall, the findings demonstrate that voice technologies can reproduce existing accent biases both by encouraging users to suppress regional features and by activating established social stereotypes through synthetic voices. The study positions sociolinguistics as a bridge between understanding human-AI interaction and designing inclusive technologies around users' own linguistic priorities.
| Comments: | 50 pages, 5 figures |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| ACM classes: | H.5.2; I.2.7; J.5; J.4 |
| Cite as: | arXiv:2610.38005 [cs.HC] |
| (or arXiv:2610.38005v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38005 arXiv-issued DOI via DataCite |
Submission history
From: Kevin Tang [view email]
[v1]
Tue, 27 Oct 2026 21:56:56 UTC (2,128 KB)
来源：arXiv cs.AI · arxiv.org