@inproceedings{upadhye-etal-2025-spacer,
title = "{SPACER}: A Parallel Dataset of Speech Production And Comprehension of Error Repairs",
author = "Upadhye, Shiva and
Li, Jiaxuan and
Futrell, Richard",
editor = "Kuribayashi, Tatsuki and
Rambelli, Giulia and
Takmaz, Ece and
Wicke, Philipp and
Li, Jixing and
Oh, Byung-Doh",
booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
month = may,
year = "2025",
address = "Albuquerque, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.cmcl-1.19/",
doi = "10.18653/v1/2025.cmcl-1.19",
pages = "144--154",
ISBN = "979-8-89176-227-5",
abstract = "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 speech 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 the integrated approach of language production and comprehension."
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%0 Conference Proceedings
%T SPACER: A Parallel Dataset of Speech Production And Comprehension of Error Repairs
%A Upadhye, Shiva
%A Li, Jiaxuan
%A Futrell, Richard
%Y Kuribayashi, Tatsuki
%Y Rambelli, Giulia
%Y Takmaz, Ece
%Y Wicke, Philipp
%Y Li, Jixing
%Y Oh, Byung-Doh
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-227-5
%F upadhye-etal-2025-spacer
%X 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 speech 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 the integrated approach of language production and comprehension.
%R 10.18653/v1/2025.cmcl-1.19
%U https://aclanthology.org/2025.cmcl-1.19/
%U https://doi.org/10.18653/v1/2025.cmcl-1.19
%P 144-154
Markdown (Informal)
[SPACER: A Parallel Dataset of Speech Production And Comprehension of Error Repairs](https://aclanthology.org/2025.cmcl-1.19/) (Upadhye et al., CMCL 2025)
ACL