Identifying gender in fricatives: A comparison of human performance with a classification based on Cepstral coefficients.
Bibliographic record
Abstract
A recent gender identification study using Cepstral coefficients extracted from voiced and voiceless fricatives from four different places of articulation yielded 78% correct classification in a linear discriminant analysis (LDA) [Spinu and Lilley (2010)]. This high accuracy was somewhat unexpected, as previous cross-linguistic studies of fricatives generally found no significant effects of gender on various types of acoustic measures. The current study investigates whether humans can identify gender based solely on frication noise as successfully as the LDA. 30 English-speaking undergraduates were asked to identify the gender of fricatives produced by 10 speakers (5 males and 5 females)—a subset of the sounds used in the LDA study. The analysis, currently in progress, suggests that humans may not be as successful as the LDA. These findings are expected to shed light on both (a) the differential distribution of acoustic features related to gender in fricatives, and (b) the extent to which these features are available/employed in human perception.
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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".