The duration of inter-speaker pauses is a pragmatically salient aspect of conversation that is affected by linguistic and non-linguistic context. Theories of conversational turn-taking imply that, due to listener entrainment to the flow of syllables, a higher speech rate will be associated with shorter turn-transition times (TTT). Previous studies have found conflicting evidence, however, some of which may be due to methodological differences. In order to test the relationship between speech rate and TTT, and how this may be modulated by other dialogue factors, we used question-answer sequences from spontaneous conversational corpora in Dutch and English. As utterance-final lengthening is a local cue to turn endings, we also examined the impact of utterance-final syllable rhyme duration on TTT. Using mixed-effect linear regression models, we observed evidence for a positive relationship between speech rate and TTT: thus, a higher speech rate is associated with longer TTT, contrary to most theoretical predictions. Moreover, for answers following a pause (“gaps”) there was a marginal interaction between speech rate and final rhyme duration, such that relatively long final rhymes are associated with shorter TTT when foregoing speech rate is high. We also found evidence that polar (yes/no) questions are responded to with shorter TTT than open questions, and that direct answers have shorter TTT than responses that do not directly answer the questions. Moreover, the effect of speech rate on TTT was modulated by question type. We found no predictors of the (negative) TTT for answers that overlap with the foregoing questions. Overall, these observations suggest that TTT is governed by multiple dialogue factors, potentially including the salience of utterance-final timing cues. Contrary to some theoretical accounts, there is no strong evidence that higher speech rates are consistently associated with shorter TTT.

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions.

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