Effects of consonant environment on vowel formant patterns
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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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".