Vocal-analysis software could help spot autism

The researchers, led by Prof D Kimbrough Oller, demonstrated that the system could automatically analyse recorded sounds from infants and children to predict, with 86 per cent accuracy, which of them might have autism.

To reach that conclusion, they analysed 1,486 recordings from 232 children using an algorithm based on the 12 acoustic parameters associated with vocal development. The most important of these parameters proved to be the ones targeting syllabification − the ability of children to produce well-formed syllables with rapid movements of the jaw and tongue. Infants show voluntary control of syllabification and voice in the first months of life, and refine this skill as they acquire language.

While typically developing children and those with language delays showed statistically significant development of the parameters, the austistic children did not.

Although aberrations in the speech (or lack of it) of children with autism-spectrum disorders has been examined by researchers and clinicians for more than 20 years, vocal characteristics are not included in standard criteria for diagnosis of autism-spectrum disorders (ASDs), said Steven F Warren, professor of applied behavioural science at the University of Kansas, who contributed to the research.