Is nonverbal behavior during conversation related to perceived fluency?
Bibliographic record
Abstract
Second language (L2) students typically aim to be effective language users, which involves learning both verbal and nonverbal aspects of communication.One aspect of effective language use is fluency, which Segalowitz (2010) described in three ways.The first is cognitive fluency, which is efficiency in a speaker's operation of underlying production processes, for example, at the level of planning, monitoring, and executing an utterance.The second is utterance fluency, which refers to the observable speech features produced by a speaker, such as their pauses or repetitions.Finally, defined as "a judgment made about speakers based on impressions drawn from their speech" (Segalowitz, 2010, p. 48), perceived fluency concerns how a speaker's speech, such as its fluidity or smoothness, impacts the listener.Our focus is on perceived fluency and its previously underexplored link with L2 speakers' nonverbal behavior.When it comes to observable dimensions of L2 performance that contribute to a speaker's perceived fluency, listeners tend to primarily rely on temporal dimensions of speech such as articulation rate, pausing, and repair in the form of repetitions and self-corrections (Bosker et al., 2013;Kahng, 2018;Saito et al., 2018;Williams & Korko, 2019).Other linguistic dimensions that underpin perceived fluency include grammar and pronunciation, where speakers' morphological and syllable structure errors are associated with lower perceived fluency for listeners (Rossiter, 2009;Suzuki & Kormos, 2020).In terms of the relative weight of various speech characteristics, according to a recent meta-analysis (Suzuki et al., 2021) listeners most strongly associate perceived fluency with articulation speed and pause frequency as opposed to other linguistic dimensions.Considering that up to 60% of variance in perceived fluency in Suzuki et al.'s extensive metadata was unexplained through measures of speaking speed, pausing, and repair, both L2 students and their teachers might find it useful to know which other aspects of communication are tied to perceived fluency, making a speaker appear more or less fluent to listeners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".