The application of a psychophysical difference metric to perceptual similarity judgments in vowels
Bibliographic record
Abstract
Models of cross-language speech perception have had limited success in predicting the discriminability or perceptual similarity of non-native contrasts. These failures may be attributed partly to an inability to quantify the phonetic differences between non-native speech sounds. This study attempted to quantify such gross psychophysical differences between speech sounds, specifically by utilizing dynamic time warping (DTW) on human factor cepstral coefficients to compare the spectrum of the entire length of the speech sounds in question. This technique has been successfully applied to account for the discriminability of different non-native consonant contrasts [Harnsberger, J. D., Shrivastav, R., and Skowronski, M.; J. Acous. Soc. Am. 117, 2460, 2005]. This study extends this work to perceptual similarity judgments of vowels. Specifically, twenty native speakers of English were presented with all possible pairings of ten vowels produced by two speakers of English. Subjects were asked to rate their similarity on a seven point scale. The resulting similarity scores were then compared with the output matrix of the DTW psychophysical difference metric for the same stimulus materials. The results showed a significant correlation (r=.60**) between the two measures, demonstrating the efficacy of the metric with a greater range of stimulus types and tasks.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".