Modeling consonant-context effects in a large database of spontaneous speech recordings
Bibliographic record
Abstract
Given recent interest in the analysis of naturally produced spontaneous speech, a large database of speech samples from the Canadian Maritimes was collected, processed, and analyzed with the primary aim of examining vowel-inherent spectral change in formant trajectories. Although it takes few resources to collect a large sample of audio recordings, the analysis of spontaneous speech introduces a number of difficulties compared to that of laboratory citation speech: Surrounding consonants may have a large influence on vowel formant frequencies and the distribution of consonant contexts is highly unbalanced. To overcome these problems, a statistical procedure inspired by that of Broad and Clermont [(2014). J. Phon. 47, 47-80] was developed to estimate the magnitude of both onset and coda effects on vowel formant frequencies. Estimates of vowel target formant frequencies and the parameters associated with consonant-context effects were allowed to vary freely across the duration of the vocalic portion of a syllable which facilitated the examination of vowel-inherent spectral change. Thirty-five hours of recorded speech samples from 223 speakers were automatically segmented and formant-frequency values were measured for all stressed vowels in the database. Consonant effects were accounted for to produce context-normalized vowel formant frequencies that varied across time.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".