The recorder's paradox: Balancing high-quality recordings with spontaneous speech in noisy recording environments
Bibliographic record
Abstract
While sociophonetic analysis requires high-quality sound recordings, sociolinguistic interviews (Labov 1984) are often conducted in uncontrolled, natural environments to elicit casual speech (Tagliamonte 2006). The effects of room acoustics and background noise on formant measurements, however, have never been systematically examined. To empirically investigate how ambient noise affects recording quality and measurements, a male speaker of English was simultaneously recorded to multiple devices reading 260 carrier phrases. Three naturally occurring background noise conditions ( + 20dB, + 10dB, and + 0dB SNR) were created in Praat 5.4 (Boersma and Weenink 2014) and mixed with the original audio recordings. 10,028 measurements of F1 and F2 were taken at the temporal midpoint of each vowel in each of the four recordings using LPC analysis in Praat. Pearson's r tests in R (R Core Team, 2014) assessed the correlation between measurements from each recording. Main results reveal positive correlations between “noiseless” and + 20dB, and + 10dB SNR conditions for each device, all vowels and both formants. When the signal was not appreciably louder than the background noise (i.e. + 0 dB SNR) correlations significantly weakened. These findings are discussed as they relate to sociolinguistic investigations that need to balance high-fidelity recordings with noisier speaking environments.
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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 | 0.050 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".