Listener Expectations and Gender Bias in Nonsibilant Fricative Perception
Bibliographic record
Abstract
The nonsibilant English fricatives /f/ and /θ / are known to be acoustically nonrobust. Using /f/ and /θ/ stimuli produced in CV, VCV, and VC syllables in /i α u/ contexts spoken by 10 talkers (5 male), we first replicate previous research suggesting that the most robust cues to this contrast are in the formant transitions in adjacent vowels. We also demonstrate vowel and syllable contextual differences that point to the contrast being most robust in /u/ contexts. In a series of perception experiments we go on to demonstrate effects of bias on perception of /f/ and /θ/ that derive from the uninformative nature of the frication noise, making them vulnerable to misperception in general, and especially in low-saliency contexts where the formant transition information is less robust. In experiment 1, listeners' classification of /f/ and /θ/ demonstrated a general bias to respond /f/ for fricatives produced by females and /θ/ for those produced by males. We hypothesize that the perceived concentrations of spectral energy in the fricative are shifted based on the concentration of energy in the vowel, which depend on a talker's gender. In experiment 2, vowel and frication noise portions were cross-spliced to probe this effect, resulting in the same gender-based bias. In a final experiment the vocalic information was removed and only the frication noise was presented to listeners for classification. In this task there was a general bias for /f/, regardless of the talker gender. Overall we demonstrate topdown gender effects in perception that originate in the strong indexical properties of adjacent vowels rather than being present in the frication noise itself.
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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".