Classification of English vowels using speech evoked potentials
Bibliographic record
Abstract
The objective of this study is to investigate whether Speech Evoked Potentials (SpEPs), which are auditory brainstem responses to speech stimuli, contain information that can be used to distinguish different speech stimuli. Previous studies on brainstem SpEPs show that they contain valuable information about auditory neural processing. As such, SpEPs may be useful for the diagnosis of central auditory processing disorders and language disability, particularly in children. In this work, we examine the spectral amplitude information of both the Envelope Following Response, which is dominated by spectral components at the fundamental (F0) and its harmonics, and Frequency Following Response, which is dominated by spectral components in the region of the first formant (F1), of SpEPs in response to the five English language vowels (\a\,\e\,\ae\,\i\,\u\). Using spectral amplitude features, a classification accuracy of 78.3% is obtained with a linear discriminant analysis classifier. Classification of SpEPs demonstrates that brainstem neural responses in the region of F0 and F1 contain valuable information for discriminating vowels. This result provides an insight into human auditory processing of speech, and may help develop improved methods for objectively assessing central hearing impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".