An evaluation of noise on LPC-based vowel formant estimates: Implications for sociolinguistic data collection
Bibliographic record
Abstract
Abstract Current trends in sociophonetic data analysis indicate a shift to entirely automatic measurements of spectral properties using programs like Praat. While such practices are useful for the rapid collection of acoustic data from large corpora, they, by default do not permit human analysts to provide quality control or make hand corrected measurements when needed. Under ideal signal-to-noise conditions, such as in a sound-proof room, this may not be a problem. However, analysis of audio recordings made in acoustically-uncontrolled environments, like many standard sociolinguistic interviews, are arguably susceptible to spurious estimates using automated routines. This paper presents the results of a highly controlled noise-interference experiment designed to examine the effects of different types of noise at varying signal-to-noise levels on automated LPC-based vowel formant measurements made in Praat. Findings indicate that some noises are more detrimental than others, affect some formant frequencies more than others and that louder noises make it inappropriate to conduct an analysis of F1 and F2. Results are discussed and suggestions for better practices in recording sociolinguistic interviews for sociophonetic data collection are presented.
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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 | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".