Real-time speech adaptations in conversations between human interlocutor and AI confederate
Bibliographic record
Abstract
Compared to human-directed adaptations, less is known about how humans adjust their speech for intelligibility benefits while interacting with an AI-powered voice interface. In this study, we investigate human speech adaptations in human-to-human versus human-to-AI unscripted conversations. Specifically, we examine the production of words containing intervocalic /t-d/ in a conversation between a speaker who distinguishes these two stops (e.g., metal–medal) and a speaker (“flapper”) who merges the two stops into a flap /ɾ/. We predict that misperceptions of intervocalic /t-d/ may cause confusions, thus motivating adaptations. We record native Canadian-English speakers (flappers) while playing a video game on Zoom in two conversation settings: with (1) a human non-flapper and (2) an AI non-flapper (computer-generated speech). Acoustic analyses of the productions by human flapper speakers include features specific to stop-flap distinctions as well as global features (e.g., overall duration). In both human- and AI-directed speech, we expect human interlocutors to change flapped productions to stops to enhance intelligibility, particularly late in the conversation. Moreover, we expect differences between human- and AI-directed adaptations, with the former dominantly employing sound-specific features and the latter relying more on global hyperarticulation. Understanding these interlocutor-oriented adaptations may inform the technology behind human-computer interfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".