Auditory Categories & Laryngoscopic/Ultrasound images in the "iPA Phonetics" App
Bibliographic record
Abstract
iPA Phonetics is an iOS application that illustrates the sounds and articulations of an expanded version of the IPA chart. The App gives users of Apple iOS mobile electronic devices the ability to access and compare (and, through matching games, to test their knowledge of) phonetic symbols and sounds together with their visual production correlates, including video of the oral vocal tract and laryngoscopic video and ultrasound of the laryngeal vocal tract. The App is entirely self-contained. The chart format follows an elaborated IPA chart ( Handbook of Phonetic Sciences, 2010). Ultrasound images were captured using a GE portable LOGIQe R5.0.1 system with an 8C-RS probe to image supraglottal laryngeal involvement (e.g. for Glottal stop) and with an 12L-RS straight-line probe at a relatively shallow 2-4 cm depth on the neck and about 2-4 cm of the vertical dimension to image larynx height changes. This is a novel laryngeal technique that differs from the approach usually taken in oral lingual ultrasound data capture. This free App’s purpose is to introduce users of phonetic symbolization, via iPad/iPhone technology, to the auditory inventory of possible speech sounds of the languages of the world and to how each sound is physically articulated. Each Consonant or Vowel category can be listened to and viewed in the form of close-up oral-endoscopic videos of the vocal tract. Images/audio may be sped up or slowed down or expanded to full screen. Pharyngeal/Epiglottal and Glottal articulations are also accompanied by laryngeal ultrasound images, for comparison with the laryngoscopic videos. The App also has a Voice Quality page with clickable oral and laryngeal categories, which orients the categories on a static graphic of the vocal tract. How users can interpret and compare auditory categories and images in the database, across Consonants, Vowels and Voice Qualities, will be demonstrated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.195 | 0.075 |
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".