Fonetix-speech articulation and hearing perception software
Bibliographic record
Abstract
For human speech perception, audio-visual interaction is prominent. Fonetix is a multimedia software kit for the Web being developed for improving speech and hearing perception. The initial phase involved the development or modification of existing systems to accommodate interactive repositories of audio and video. This included the display of animated midsagittal views of the vocal tract in real-time on personal computers. Several existing systems allows the patients to experiment with notions of pitch and volume but are of limited beneficial without a speech pathologist present to assist in improving the person's speech. The addition of animation allows the person to see how the mouth, tongue, teeth, and lips (oral cavity), are used in producing of phonemes or isolated words. The Fonetix system permits users to see in a graphical and an easily comprehensible way how closely they approach the targeted speech pattern. Fonetix also provides valuable comments and suggestions on the modification to articulation needed to improve speech. This is useful for patients in rehabilitation, who have problems with speech, resulting from various injuries such as, stroke, brain damage, hearing loss, and head injuries, and also for students who are learning English as a Second Language (ESL). The types of tools being developed are intended to augment current practices and techniques being used by speech pathologists.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.176 | 0.044 |
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".