Acoustic and phonetic discrimination in speech using event-related potentials: A mismatch negativity paradigm
Bibliographic record
Abstract
The interaction of acoustic and phonetic discrimination in speech was studied using event-related potentials (ERPs). Participants consisted of 14 neurologically normal right-handed adults. Data were recorded in response to strong and weak acoustic changes signaling consonant distinctions, using a 64-channel encephalogram and a mismatch negativity (MMN) paradigm. The MMN is an automatic response evoked by stimulus change and is elicited when a train of repeated stimuli (standard) is interrupted by an oddball (deviant) stimulus. Stimuli consisted of one standard and two deviants that differed in their acoustic difference from the standard. All three stimuli were synthetic speech syllables in which F2 was manipulated to create a continuum between the syllables ‘‘ba’’ and ‘‘da.’’ The standard was the endpoint item ‘‘da.’’ The ‘‘strong deviant’’ was the ‘‘ba’’ endpoint item; the ‘‘weak deviant’’ was a more intermediate ‘‘ba’’ item, and therefore acoustically closer to the standard. MMNs were identified for both deviants, but the amplitude was smaller for the weak deviant condition. The data suggest that MMNs are sensitive to both phonetic change and also the magnitude of the acoustic difference within this change. The results are discussed with respect to theories of phonetic and acoustic discrimination in speech perception.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".