Acoustic and perceptual speaker normalization
Bibliographic record
Abstract
An attempt was made to evaluate the performance of several methods for speaker normalization in both the acoustic and the perceptual domain of speech. This was done by comparing the acoustic distributions applied to the same vowel data and further by comparing the acoustic distributions to the perceptual distributions of the vowel data. The normalization methods included, among others, extrinsic (e.g., z-score transformation) and intrinsic methods (Bark transformation), and formant weighting (F2) and correction (F3−F2). To obtain the acoustic distributions, the normalization methods were applied to F0 and formant data from monophthong vowels in /sVs/ context of male and female speakers of Standard Dutch. The perceptual distributions were obtained through an experiment with phonetically trained listeners, whose task was to judge each vowel’s height, place of constriction and amount of rounding/spreading. The acoustic and perceptual distributions were compared using correlational- and cluster-analysis techniques. When describing the results of these tests, the focus will be on the extent to which the variation and overlap are the same in the acoustic and perceptual domains, and which normalization methods show a pattern of overlap and variation most similar in both domains. [Work supported by The Netherlands Organization for Research (NWO).]
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".