Caitlin Decuyper


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  • Rodd, J., Decuyper, C., Bosker, H. R., & Ten Bosch, L. (2020). A tool for efficient and accurate segmentation of speech data: Announcing POnSS. Behavior Research Methods. Advance online publication. doi:10.3758/s13428-020-01449-6.


    Despite advances in automatic speech recognition (ASR), human input is still essential to produce research-grade segmentations of speech data. Con- ventional approaches to manual segmentation are very labour-intensive. We introduce POnSS, a browser-based system that is specialized for the task of segmenting the onsets and offsets of words, that combines aspects of ASR with limited human input. In developing POnSS, we identified several sub- tasks of segmentation, and implemented each of these as separate interfaces for the annotators to interact with, to streamline their task as much as possible. We evaluated segmentations made with POnSS against a base- line of segmentations of the same data made conventionally in Praat. We observed that POnSS achieved comparable reliability to segmentation us- ing Praat, but required 23% less annotator time investment. Because of its greater efficiency without sacrificing reliability, POnSS represents a distinct methodological advance for the segmentation of speech data.
  • Sjerps, M. J., Decuyper, C., & Meyer, A. S. (2020). Initiation of utterance planning in response to pre-recorded and “live” utterances. Quarterly Journal of Experimental Psychology, 73(3), 357-374. doi:10.1177/1747021819881265.


    In everyday conversation, interlocutors often plan their utterances while listening to their conversational partners, thereby achieving short gaps between their turns. Important issues for current psycholinguistics are how interlocutors distribute their attention between listening and speech planning and how speech planning is timed relative to listening. Laboratory studies addressing these issues have used a variety of paradigms, some of which have involved using recorded speech to which participants responded, whereas others have involved interactions with confederates. This study investigated how this variation in the speech input affected the participants’ timing of speech planning. In Experiment 1, participants responded to utterances produced by a confederate, who sat next to them and looked at the same screen. In Experiment 2, they responded to recorded utterances of the same confederate. Analyses of the participants’ speech, their eye movements, and their performance in a concurrent tapping task showed that, compared with recorded speech, the presence of the confederate increased the processing load for the participants, but did not alter their global sentence planning strategy. These results have implications for the design of psycholinguistic experiments and theories of listening and speaking in dyadic settings.
  • Meyer, A. S., Alday, P. M., Decuyper, C., & Knudsen, B. (2018). Working together: Contributions of corpus analyses and experimental psycholinguistics to understanding conversation. Frontiers in Psychology, 9: 525. doi:10.3389/fpsyg.2018.00525.


    As conversation is the most important way of using language, linguists and psychologists should combine forces to investigate how interlocutors deal with the cognitive demands arising during conversation. Linguistic analyses of corpora of conversation are needed to understand the structure of conversations, and experimental work is indispensable for understanding the underlying cognitive processes. We argue that joint consideration of corpus and experimental data is most informative when the utterances elicited in a lab experiment match those extracted from a corpus in relevant ways. This requirement to compare like with like seems obvious but is not trivial to achieve. To illustrate this approach, we report two experiments where responses to polar (yes/no) questions were elicited in the lab and the response latencies were compared to gaps between polar questions and answers in a corpus of conversational speech. We found, as expected, that responses were given faster when they were easy to plan and planning could be initiated earlier than when they were harder to plan and planning was initiated later. Overall, in all but one condition, the latencies were longer than one would expect based on the analyses of corpus data. We discuss the implication of this partial match between the data sets and more generally how corpus and experimental data can best be combined in studies of conversation.

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