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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.


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.


La veille de la cybersécurité

#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.


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.