The effect of perceived human-likeness on voice-user interface–directed speech
Bibliographic record
Abstract
Many interact daily with voice–user interfaces (VUIs), but acoustic research on VUI-directed speech (VDS) is relatively new. Prior work indicates intensity and F0 correlate with VDS [Cohn et al., 2022, JPhon 90]. Multiple acoustic variables of VDS were analyzed to explore if VDS is a register distinct from human-directed speech (HDS) and whether perceived human-likeness of VUI voices affects VDS characteristics. 27 participants’ Zoom recordings of 13 pre-scripted prompts and pre-recorded responses from two Amazon AWS-Polly-generated VUI voices (rated for human-likeness independently and by participants) were acoustically analyzed for word-initial voiceless plosives voice onset time (VOT), pitch variation, and vowel quality and quantity. Results of linear mixed-effects models indicate evidence of VDS-specific acoustic characteristics, some of which are affected by participants’ perceived human-likeness of the voices. Differences in pre-exposure and VUI interactions occur for /p/ VOT in consonant clusters, vowel duration (except /ɪ/), and /ɑ/ F2. Statistical differences are found for /p/ VOT in consonant clusters and vowel quality (e.g., /ɑ/ F1 and F2, and /ɪ/ F2) based on perceived human-likeness by participants. This study contributes to the growing VDS work examining how humans speak with devices and what affects VDS, which may influence considerations of VUI voice development.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".