Suitability of self-recordings and video calls: Vowel formants and nasal spectra
Bibliographic record
Abstract
When the COVID-19 pandemic halted in-person data collection, many linguists adopted new online technologies to replace traditional methods. These included VoIP video conferencing apps like Zoom, which allow live interaction with participants, as well as user-led options in which participants record themselves using personal computers or smartphones and then email or otherwise transfer the sound files to researchers online. This study evaluated the suitability of such recordings for phonetic analysis of vowel space configurations, mergers, and nasalization by comparing simultaneous recordings from several popular personal devices (Macbook, PC laptop, iPad, iPhone, Android phone) and video call apps (Zoom, Skype, Teams) to those taken from professional equipment (H4n field recorder, Focusrite with table-top microphone). All personal devices and apps conveyed vowel arrangements and nasalization patterns relatively faithfully (especially laptops), but absolute measurements varied, particularly for the female speaker and in the 750-1500 Hz range, which affected the locations (F1xF2) of low and back vowels and reduced nasalization measurements (A1-P0) for the female's pre-nasal vowels. Based on these results we assess the validity of remote recording using these devices and offer recommendations for best practices for collecting high fidelity acoustic phonetic data from a distance.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".