Effects of consonant environment on vowel formant patterns
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Bibliographic record
Abstract
A significant body of evidence has accumulated indicating that vowel identification is influenced by spectral change patterns. For example, a large-scale study of vowel formant patterns showed substantial improvements in category separability when a pattern classifier was trained on multiple samples of the formant pattern rather than a single sample at steady state [J. Hillenbrand et al., J. Acoust. Soc. Am. 97, 3099-3111 (1995)]. However, in the earlier study all utterances were recorded in a constant /hVd/ environment. The main purpose of the present study was to determine whether a close relationship between vowel identity and spectral change patterns is maintained when the consonant environment is allowed to vary. Recordings were made of six men and six women producing eight vowels (see text) in isolation and in CVC syllables. The CVC utterances consisted of all combinations of seven initial consonants (/h,b,d,g,p,t,k/) and six final consonants (/b,d,g,p,t,k/). Formant frequencies for F1-F3 were measured every 5 ms during the vowel using an interactive editing tool. Results showed highly significant effects of phonetic environment. As with an earlier study of this type, particularly large shifts in formant patterns were seen for rounded vowels in alveolar environments [K. Stevens and A. House, J. Speech Hear. Res. 6, 111-128 (1963)]. Despite these context effects, substantial improvements in category separability were observed when a pattern classifier incorporated spectral change information. Modeling work showed that many aspects of listener behavior could be accounted for by a fairly simple pattern classifier incorporating F0, duration, and two discrete samples of the formant pattern.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it