Pattern Playback revisited: Unvoiced stop consonant perception
Bibliographic record
Abstract
Among the most influential publications in speech perception is Liberman, Delattre, and Cooper's [Am. J. Phys. 65, 497-516 (1952)] report on the identification of synthetic, voiceless stops generated by the Pattern Playback. Their map of stop consonant identification shows a highly complex relationship between acoustics and perception. This complex mapping poses a challenge to many classes of relatively simple pattern recognition models which are unable to capture the original finding of Liberman et al. that identification of /k/ was bimodal for bursts preceding front vowels but otherwise unimodal. A replication of this experiment was conducted in an attempt to reproduce these identification patterns using a simulation of the Pattern Playback device. Examination of spectrographic data from stimuli generated by the Pattern Playback revealed additional spectral peaks that are consistent with harmonic distortion characteristic of tube amplifiers of that era. Only when harmonic distortion was introduced did bimodal /k/ responses in front-vowel context emerge. The acoustic consequence of this distortion is to add, e.g., a high-frequency peak to midfrequency bursts or a midfrequency peak to a low-frequency burst. This likely resulted in additional /k/ responses when the second peak approximated the second formant of front vowels. Although these results do not challenge the main observations made by Liberman et al. that perception of stop bursts is context dependent, they do show that the mapping from acoustics to perception is much less complex without these additional distortion products.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".