Cross-dialectal analysis of English pitch range in male voices and its influence on aesthetic judgments of speech
Bibliographic record
Abstract
Abstract This study focuses on the differences in pitch register and pitch span across five accents of English, and investigates their potential effects on judgements of speech. We recorded two male middle-aged speakers for each of the following accents of English: Brighton, Manchester, Perth, New Jersey and Edmonton. Then, we modified pitch register in selected spontaneous speech recordings by raising the overall pitch in the recordings by 5 Hz and 15 Hz using Praat. The entire material was then randomized and prepared for an online survey. A group of 50 respondents (30 female, 20 male) who were non-native speakers of English were asked in a blind study to evaluate both the unmodified and modified recordings on a 7-point Likert scale in terms of their perceived attractiveness, friendliness, prestige and self-confidence. Overall, it has been found that pitch span can be a telling cue when evaluating perceived friendliness for both gender groups, while pitch register can affect male listeners in evaluating attractiveness and self-confidence. Finally, it seems that there is a an upper limit for what listeners can aesthetically accept in terms of pitch register, as the recordings with highest registers were disfavored by our respondents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".