Acoustic Exaggeration Enhances Speech Discrimination in Young Autistic Children
Bibliographic record
Abstract
Child-directed speech (CDS), which amplifies acoustic and social features of speech during interactions with young children, promotes typical phonetic and language development. In autism, both behavioral and brain data indicate reduced sensitivity to human speech, which predicts absent, decreased, or atypical benefits of exaggerated speech signals such as CDS. This study investigates the impact of exaggerated fundamental frequency (F0) and voice-onset time on the neural processing of speech sounds in 22 Chinese-speaking autistic children aged 2-7 years old with a history of speech delays, compared with 25 typically developing (TD) peers. Electroencephalography (EEG) data were collected during passive listening to exaggerated and non-exaggerated syllables. A time-resolved multivariate pattern analysis (MVPA) was used to evaluate the potential effects of acoustic exaggeration on syllable discrimination in terms of neural decoding accuracy. For non-exaggerated syllables, neither the autism nor the TD group achieved above-chance decoding accuracy. In contrast, for exaggerated syllables, both groups achieved above-chance decoding, indicating significant syllable discrimination, with no difference in accuracy between the autism and TD groups. However, the temporal generalization patterns in the MVPA results revealed distinct neural mechanisms supporting syllable discrimination between the groups. Although the TD group demonstrated a left-hemisphere advantage for decoding and generalization, the autism group displayed similar decoding patterns between hemispheres. These findings highlight the potential of selective acoustic exaggeration to support speech learning in autistic children, underscoring the importance of tailored, sensory-based interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".