构建语音纠错平行数据集，揭示说者与听者纠错策略差异。
SPACER: A Parallel Dataset of Speech Production And Comprehension of Error Repairs
- 从语音语料中提取单字替换错误，采集说话人自修正与听者反应。
- 说话人更倾向修正语义/音似偏差大的错误，听者则关注音近或不合上下文的错误。
- 首个可对比生产与理解纠错行为的平行数据集，适合语言认知研究者。
语音错误是交流中的自然现象，但通常不会导致沟通失败，因说话人和听者均可察觉并纠正错误。尽管已有研究分别探讨了语音生成与理解中的错误监测与修正，但因缺乏平行数据，两者整合研究受限。本文提出SPACER，一个捕捉自然语音错误在生成与理解中如何被修正的平行数据集。研究聚焦于来自Switchboard语料库的单字替换错误，包含说话人自修复记录及听者在离线文本编辑实验中的回应。探索性分析显示纠错策略存在不对称性：说话人更可能修正语义或音位偏离较大的错误；听者则倾向于纠正音近于合理替代项或不符合前文语境的错误。该数据集为未来整合研究语言生成与理解提供了基础。
原文摘要 · Abstract (English)
Speech errors are a natural part of communication, yet they rarely lead to complete communicative failure because both speakers and comprehenders can detect and correct errors. Although prior research has examined error monitoring and correction in production and comprehension separately, integrated investigation of both systems has been impeded by the scarcity of parallel data. In this study, we present SPACER, a parallel dataset that captures how naturalistic speech errors are corrected by both speakers and comprehenders. We focus on single-word substitution errors extracted from the Switchboard corpus, accompanied by speaker's self-repairs and comprehenders' responses from an offline text-editing experiment. Our exploratory analysis suggests asymmetries in error correction strategies: speakers are more likely to repair errors that introduce greater semantic and phonemic deviations, whereas comprehenders tend to correct errors that are phonemically similar to more plausible alternatives or do not fit into prior contexts. Our dataset enables future research on integrated approaches toward studying language production and comprehension.
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