Acoustic and prosodic analysis of pre-verbal vocalizations of 18-month old toddlers with autism spectrum disorder.
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) covers a wide spectrum of symptoms with the main \nones relating to problems with social communication and interaction. Definite ASD \ndiagnosis is based on the presence of certain symptoms and their severity levels and, \naccording to current standards, occurs typically at 36 months of age. Recent statistics \nshow that about 1 in 68 children are diagnosed with autism and there is a recurrence \nrate of 18.7% for the biological siblings of autistic individuals. As such, early detection \nis critical, as it may allow for intense therapy to be initiated, thus tapping into a \nyoung brain’s plasticity properties and increasing odds of success. Today, researchers \nand clinicians have joined efforts to understand and identify new markers of the disorders, \nthus allowing for early diagnosis, ideally around 18 months of age. To this end, \nacoustic analysis of toddler vocalizations has emerged as a promising area, even for \npre-verbal children. Prosodic and acoustic disorders have been reported for babble and \nspeech-like vocalizations. As such, pitch, energy and voice quality related features have \nbeen explored for early ASD diagnosis. In this work, we build upon these findings and \npropose the use of wavelet-based and speech modulation spectral features for ASD diagnosis \nbased not only on speech-like verbalizations, but also on cries, laughs, and other \nsounds made by the toddlers. We show that the proposed features are complementary \nto existing ones and, on a cohort of forty-three 18-month old toddlers, a support vector \nmachine classifier was capable of correctly discriminating the ASD group from the \ntypically-developing toddlers with accuracies above 80%, thus outperforming existing \nmethods. More importantly, we show that with these new features, vocalizations such \nas cries, squeals, whines and shouts showed to be more discriminative than babble and \nspeech-like vocalizations. It is hoped that these findings will lead to more accurate \nearly diagnosis of ASD symptoms.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".