The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
What makes speech sound fluent? The contributions of pauses, speed and repairs
Open accessAbstract
The oral fluency level of an L2 speaker is often used as a measure in assessing language proficiency. The present study reports on four experiments investigating the contributions of three fluency aspects (pauses, speed and repairs) to perceived fluency. In Experiment 1 untrained raters evaluated the oral fluency of L2 Dutch speakers. Using specific acoustic measures of pause, speed and repair phenomena, linear regression analyses revealed that pause and speed measures best predicted the subjective fluency ratings, and that repair measures contributed only very little. A second research question sought to account for these results by investigating perceptual sensitivity to acoustic pause, speed and repair phenomena, possibly accounting for the results from Experiment 1. In Experiments 2–4 three new groups of untrained raters rated the same L2 speech materials from Experiment 1 on the use of pauses, speed and repairs. A comparison of the results from perceptual sensitivity (Experiments 2–4) with fluency perception (Experiment 1) showed that perceptual sensitivity alone could not account for the contributions of the three aspects to perceived fluency. We conclude that listeners weigh the importance of the perceived aspects of fluency to come to an overall judgment.
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Mixed-Effects Models in Sand S-PLUS
Pinheiro, J. C., & Bates, D. M. (2000) · Statistics and Computing · doi:10.1007/978-1-4419-0318-1 · Cited by 11,187
Mixed-Effects Models in S and S-PLUS
(2001) · Technometrics · doi:10.1198/tech.2001.s574 · Cited by 9,278
Mixed-effects modeling with crossed random effects for subjects and items
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.12.005 · Cited by 8,540
Multilevel analysis techniques and applications
Hox, J. J., Moerbeek, M., & van de Schoot, R. (2002) · Cited by 6,003
Hox, J., Moerbeek, M., & van de Schoot, R. (2010) · doi:10.4324/9780203852279 · Cited by 3,469
Analyzing Linguistic Data: A Practical Introduction to Statistics using R
Baayen, R. H. (2008) · doi:10.1017/cbo9780511801686 · Cited by 3,253
Analyzing linguistic data : a practical introduction to statistics using R
Baayen, R. H. (2008) · Cited by 2,069
Monitoring and self-repair in speech.
Levelt, W. (1983) · Cognition · doi:10.1016/0010-0277(83)90026-4 · Cited by 1,676
Thurstone, L. L. (1928) · American Journal of Sociology · doi:10.1086/214483 · Cited by 1,464
Research works printed but not linked: 3
- Gilabert R., 2007, International Review of Applied Linguistics in Language Teaching (IRAL), 45, 215
- Hulstijn J. H., 2012, Language Teaching, 29, 202
- Veenker T. J. G., 2006, FEP: A tool for designing and running computerized experiments
Cited by
TASK REPETITION AND SECOND LANGUAGE SPEECH PROCESSING
Lambert, C., Kormos, J., & Minn, D. (2017) · Studies in Second Language Acquisition · doi:10.1017/s0272263116000085 · Cited by 281
Saito, K., & Plonsky, L. (2019) · Language Learning · doi:10.1111/lang.12345 · Cited by 282
Kahng, J. (2014) · Language Learning · doi:10.1111/lang.12084 · Cited by 200
Fluency in Second Language Testing: Insights From Different Disciplines
De Jong, N. H. (2018) · Language Assessment Quarterly · doi:10.1080/15434303.2018.1477780 · Cited by 139
Spacing effects on repeated L2 task performance
Bui, G., Ahmadian, M. J., & Hunter, A.-M. (2019) · System · doi:10.1016/j.system.2018.12.006 · Cited by 106
Metadata
- Authors
- Bosker, H. R., Pinget, A.-F., Quené, H., Sanders, T., & de Jong, N. H.
- Venue
- Language Testing
- Publisher
- SAGE Publications
- Published
- 2012-10-06
- Volume
- 30
- Issue
- 2
- Pages
- 159-175
- ISSN
- 0265-5322 (ISSN-L)
- 1477-0946
- OpenAlex
- W2124678974
- Cited by
- 301
- References
- 37
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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