Speakers are able to categorize vowels based on tongue somatosensation
Bibliographic record
Abstract
Auditory speech perception enables listeners to access phonological categories from speech sounds. During speech production and speech motor learning, speakers' experience matched auditory and somatosensory input. Accordingly, access to phonetic units might also be provided by somatosensory information. The present study assessed whether humans can identify vowels using somatosensory feedback, without auditory feedback. A tongue-positioning task was used in which participants were required to achieve different tongue postures within the /e, ε, a/ articulatory range, in a procedure that was totally nonspeech like, involving distorted visual feedback of tongue shape. Tongue postures were measured using electromagnetic articulography. At the end of each tongue-positioning trial, subjects were required to whisper the corresponding vocal tract configuration with masked auditory feedback and to identify the vowel associated with the reached tongue posture. Masked auditory feedback ensured that vowel categorization was based on somatosensory feedback rather than auditory feedback. A separate group of subjects was required to auditorily classify the whispered sounds. In addition, we modeled the link between vowel categories and tongue postures in normal speech production with a Bayesian classifier based on the tongue postures recorded from the same speakers for several repetitions of the /e, ε, a/ vowels during a separate speech production task. Overall, our results indicate that vowel categorization is possible with somatosensory feedback alone, with an accuracy that is similar to the accuracy of the auditory perception of whispered sounds, and in congruence with normal speech articulation, as accounted for by the Bayesian classifier.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".