Effects of vocoding and intelligibility on the cerebral response to speech
Bibliographic record
Abstract
BACKGROUND: Degrading speech through an electronic synthesis technique called vocoding has been shown to affect cerebral processing of speech in several cortical areas. However, it is not clear whether the effects of speech degradation by vocoding are related to acoustical degradation or by the associated loss in intelligibility. Using vocoding and a parametric variation of the number of frequency bands used for the encoding, we investigated the effects of the degradation of auditory spectral content on cerebral processing of intelligible speech (words), unintelligible speech (words in a foreign language), and complex environmental sounds. RESULTS: Vocoding was found to decrease activity to a comparable degree for intelligible and unintelligible speech in most of the temporal lobe. Only the bilateral posterior temporal areas showed a significant interaction between vocoding and intelligibility, with a stronger vocoding-induced decrease in activity for intelligible speech. Comparisons to responses elicited by environmental sounds showed that portions of the temporal voice areas (TVA) retained their greater responses to voice even under adverse listening conditions. The recruitment of specific networks in temporal regions during exposure to degraded speech follows a radial and anterior-posterior topography compared to the networks recruited by exposure to speech that is not degraded. CONCLUSIONS: Different brain networks are involved in vocoded sound processing of intelligible speech, unintelligible speech, and non-vocal sounds. The greatest differences are between speech and environmental sounds, which could be related to the distinctive temporal structure of speech sounds.
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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) | 0.002 | 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".