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Dr. D. Kimbrough Oller

The LENA™ (Language Environment Analysis) system automatically labeled infant and child vocalizations from recordings. Following that step, an automatic acoustic analysis designed by Dr. D. Kimbrough Oller, professor and chair of excellence in audiology and speech-language pathology at the University of Memphis, showed – with 86 percent accuracy – that pre-verbal vocalizations of very young children with autism are distinctly different from those of typically-developing children.

The system also differentiated typically-developing children and children with autism from children with language delay, based on the automated vocal analysis.

The researchers analyzed 1,486 all-day recordings from 232 children (more than 3.1 million automatically identified child utterances) through an algorithm based on the 12 acoustic parameters associated with vocal development, as indicated in Oller’s research over a 35-year period. 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 during vocalization. Infants show voluntary control of syllabification and voice in the first months of life and refine this skill as they acquire language.

“The autistic sample showed little evidence of development on the parameters, as indicated by low correlations between the parameter values and the children's ages (from 1 to 4 years),” said Oller. “On the other hand, all 12 parameters showed statistically significant development for both typically-developing children and those with language delays.”

The research team called the findings a proof of concept that automated analysis of massive samples of vocalizations can now be included in the scientific repertoire for research on vocal development.

Although aberrations in the speech (or lack of speech) of children with autism spectrum disorders have been examined by researchers and clinicians for more than 20 years, vocal characteristics are not included in standard criteria for diagnosis of those disorders, said Steven F. Warren, professor of applied behavioral science and vice provost for research at the University of Kansas, who contributed to the study and, along with Oller, was among the first to see the potential of the technology for autism spectrum disorders screening.

"A small number of studies had previously suggested that children with autism have a markedly different vocal signature, but until now, we have been held back from using this knowledge in clinical applications by the lack of measurement technology," Warren said.

Oller also noted that a key feature of this approach is that it is totally objective. “Previous methods of screening for and diagnosis of autism have relied very heavily on the observations and judgments of parents, teachers, and professional diagnosticians,” Oller said, “all of which inevitably include subjectivity.”

The researchers predict that LENA, which allows the inexpensive collection and analysis of magnitudes of data unimagined in language research before now, could have a significant impact on the screening, assessment, and treatment of autism and the behavioral sciences in general.

“Even though the study focused on English-learning children only,” said Oller, “there is reason to believe that the technology should be applicable to children speaking languages other than English. The reason for this expectation is that the model of acoustic parameters used in the research is designed to be universal with regard to languages, focusing on features of voice and articulation of speech or speech-like sounds that occur in languages everywhere.”

The researchers noted that children with autism spectrum disorders can be diagnosed at 18 months, but the median age of diagnosis in the United States is 5.7 years.

Warren said that this technology could help pediatricians screen children for ASD to determine if a referral to a specialist for a full diagnosis is required and get those children into earlier and more effective treatments.

LENA combines a digital language processor and language analysis software. The processor fits into the pocket of specially designed children's clothing and records everything the child vocalizes, but it can reliably distinguish a child’s vocalizations from its cries and vegetative sounds, other voices, and extraneous environmental sounds.

Recordings with the device have been collected since 2006. Parents responded to public notices in the media and indicated if their children had been diagnosed with autism or language delay. A speech-language clinician employed by the project also evaluated many of the children who had a reported diagnosis of language delay. The parents of many of the children with language delay and all of the children with autism supplied documentation from the diagnosing clinicians, whose diagnoses were independent of the research.

The recordings were made by the parents at home and in the other natural environments of the children, simply by turning the recorder on and placing it in the children's clothing, where it was worn all day.

The discovery that it was possible to differentiate recordings of the autistic children from those of the typically developing children by the totally objective method of automated vocal analysis inspired the researchers to consider the possibility of earlier screening and diagnosis and earlier intervention for children with autism, Oller pointed out.

"Autism interventions are expensive and arduous. This tool may help us develop cost-effective treatments and better understand how they work and how to keep them working," said Warren.

LENA could allow parents to continue to supplement language enrichment therapy at home and assess their own effectiveness for themselves, Warren said. "In this way, LENA could function similarly to the way a pedometer measures how much exercise a person gets from walking."

Oller summed up his thoughts on the research like this: “We are now in a new era in research in vocal development. Never before was it possible to use totally automated systems to analyze massive quantities of recordings collected in children’s homes, and thus track typical development and provide the basis for significant clinical contributions to screening, to diagnosis, and to help in monitoring intervention. Now, all of that is possible, and the effects of this development will be felt in research, in education, and in the clinical setting.”