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AI Detects Autism Speech Patterns Across Different Languages - Neuroscience News

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Summary: Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages. A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies. Children with autism often talk more slowly than typically developing children, and exhibit other differences in pitch, intonation and rhythm. But those differences (called "prosodic differences'" by researchers) have been surprisingly difficult to characterize in a consistent, objective way, and their origins have remained unclear for decades. However, a team of researchers led by Northwestern scientists Molly Losh and Joseph C.Y. Lau, along with Hong Kong-based collaborator Patrick Wong and his team, successfully used supervised machine learning to identify speech differences associated with autism.


AI detects autism speech patterns across different languages

#artificialintelligence

A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies. Children with autism often talk more slowly than typically developing children, and exhibit other differences in pitch, intonation and rhythm. But those differences (called "prosodic differences'" by researchers) have been surprisingly difficult to characterize in a consistent, objective way, and their origins have remained unclear for decades. However, a team of researchers led by Northwestern scientists Molly Losh and Joseph C.Y. Lau, along with Hong Kong-based collaborator Patrick Wong and his team, successfully used supervised machine learning to identify speech differences associated with autism. The data used to train the algorithm were recordings of English- and Cantonese-speaking young people with and without autism telling their own version of the story depicted in a wordless children's picture book called "Frog, Where Are You?" The results were published in the journal PLOS One on June 8, 2022.


Infographic: World Autism Awareness Day

Al Jazeera

People across the globe observe April 2 as World Autism Awareness Day – designated by the United Nations to raise awareness about autism spectrum disorder (ASD). This year marks the 15th World Autism Awareness Day. ASD is a term used to describe a wide variety of neurological and developmental conditions. It can include trouble with speech, social interaction with others, repetitive behaviour as well as sensory aversions, such as sensitivity to sound and light. It is a condition that can affect all ethnic and socioeconomic groups, varying greatly from person to person.


Researchers develop AI algorithm for identifying autism spectrum disorder solely using video - Mental Daily

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A team at the University of Geneva in Switzerland developed an artificial intelligence algorithm capable of detecting autism spectrum disorder (ASD) in children using solely video. The study was posted online in Scientific Reports. According to researchers, the new findings may make it easier to identify cases of autism in children by analyzing short videos. "We recognize that while the diagnosis process understands the indexing of the specific behaviors, ASD also comes with broad impairments that often transcend single behavioral acts," the authors explained in their findings. "For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech."


Northwestern University Researchers Used Machine Learning To Identify Speech Patterns In Children With Autism That Were Consistent Between English And Cantonese

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According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.