The massive auditory lexical decision database: Acoustic analyses of a large-scale, single speaker corpus
Bibliographic record
Abstract
Studying speech production has allowed the discovery of numerous patterns that hold within—and, at times, differ across—speaker groups (e.g., voice-onset-time as a cue for consonant voicing). However, such work rarely explores the variation and consistencies that might be observed within comparable numbers of productions from a single speaker. In fact, acoustic analyses of speech production most often survey relatively few observations from each speaker, where high participant numbers are required to offset limited by-speaker contributions. Therefore, previous research often addresses inter-speaker variation, but by nature disallows any meaningful exploration of variation/consistency across productions from a single speaker. In tandem with the Massive Auditory Lexical Decision (MALD) study [Tucker et al., Behav. Res. 51(3), 1187–1204 (2019)], the present work serves two purposes: (1) to analyze and describe speech stimuli comprising the MALD single-speaker corpus (26 793 English words and 9592 English pseudowords, roughly 6 h of recorded speech in total), and (2) to suggest the breadth of work possible using such a corpus. As examples of the latter, analyses are provided to compare and contrast acoustic characteristics associated with varying degrees of lexical stress, and to explore the vowel space as realized in words versus pseudowords.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".