Voice acoustics allow classifying autism spectrum disorder with high accuracy
Abstract Early identification of children on the autism spectrum is crucial for early intervention with long-term positive effects on symptoms and skills. The need for improved objective autism detection tools is emphasized by the poor diagnostic power in current tools. Here, we aim to evaluate the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2023-07-01
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| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-023-02554-8 |
| _version_ | 1797784460317425664 |
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| author | Frédéric Briend Céline David Silvia Silleresi Joëlle Malvy Sandrine Ferré Marianne Latinus |
| author_facet | Frédéric Briend Céline David Silvia Silleresi Joëlle Malvy Sandrine Ferré Marianne Latinus |
| author_sort | Frédéric Briend |
| collection | DOAJ |
| description | Abstract Early identification of children on the autism spectrum is crucial for early intervention with long-term positive effects on symptoms and skills. The need for improved objective autism detection tools is emphasized by the poor diagnostic power in current tools. Here, we aim to evaluate the classification performance of acoustic features of the voice in children with autism spectrum disorder (ASD) with respect to a heterogeneous control group (composed of neurotypical children, children with Developmental Language Disorder [DLD] and children with sensorineural hearing loss with Cochlear Implant [CI]). This retrospective diagnostic study was conducted at the Child Psychiatry Unit of Tours University Hospital (France). A total of 108 children, including 38 diagnosed with ASD (8.5 ± 0.25 years), 24 typically developing (TD; 8.2 ± 0.32 years) and 46 children with atypical development (DLD and CI; 7.9 ± 0.36 years) were enrolled in our studies. The acoustic properties of speech samples produced by children in the context of a nonword repetition task were measured. We used a Monte Carlo cross-validation with an ROC (Receiving Operator Characteristic) supervised k-Means clustering algorithm to develop a classification model that can differentially classify a child with an unknown disorder. We showed that voice acoustics classified autism diagnosis with an overall accuracy of 91% [CI95%, 90.40%-91.65%] against TD children, and of 85% [CI95%, 84.5%–86.6%] against an heterogenous group of non-autistic children. Accuracy reported here with multivariate analysis combined with Monte Carlo cross-validation is higher than in previous studies. Our findings demonstrate that easy-to-measure voice acoustic parameters could be used as a diagnostic aid tool, specific to ASD. |
| first_indexed | 2024-03-13T00:40:12Z |
| format | Article |
| id | doaj.art-ee4e4ec53472453ea6146e2b66ac047a |
| institution | Directory Open Access Journal |
| issn | 2158-3188 |
| language | English |
| last_indexed | 2024-03-13T00:40:12Z |
| publishDate | 2023-07-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj.art-ee4e4ec53472453ea6146e2b66ac047a2023-07-09T11:25:48ZengNature Publishing GroupTranslational Psychiatry2158-31882023-07-011311810.1038/s41398-023-02554-8Voice acoustics allow classifying autism spectrum disorder with high accuracyFrédéric Briend0Céline David1Silvia Silleresi2Joëlle Malvy3Sandrine Ferré4Marianne Latinus5UMR 1253, iBrain, Université de Tours, INSERMUMR 1253, iBrain, Université de Tours, INSERMUniversity of Milano-Bicocca, Department of PsychologyUMR 1253, iBrain, Université de Tours, INSERMUMR 1253, iBrain, Université de Tours, INSERMUMR 1253, iBrain, Université de Tours, INSERMAbstract Early identification of children on the autism spectrum is crucial for early intervention with long-term positive effects on symptoms and skills. The need for improved objective autism detection tools is emphasized by the poor diagnostic power in current tools. Here, we aim to evaluate the classification performance of acoustic features of the voice in children with autism spectrum disorder (ASD) with respect to a heterogeneous control group (composed of neurotypical children, children with Developmental Language Disorder [DLD] and children with sensorineural hearing loss with Cochlear Implant [CI]). This retrospective diagnostic study was conducted at the Child Psychiatry Unit of Tours University Hospital (France). A total of 108 children, including 38 diagnosed with ASD (8.5 ± 0.25 years), 24 typically developing (TD; 8.2 ± 0.32 years) and 46 children with atypical development (DLD and CI; 7.9 ± 0.36 years) were enrolled in our studies. The acoustic properties of speech samples produced by children in the context of a nonword repetition task were measured. We used a Monte Carlo cross-validation with an ROC (Receiving Operator Characteristic) supervised k-Means clustering algorithm to develop a classification model that can differentially classify a child with an unknown disorder. We showed that voice acoustics classified autism diagnosis with an overall accuracy of 91% [CI95%, 90.40%-91.65%] against TD children, and of 85% [CI95%, 84.5%–86.6%] against an heterogenous group of non-autistic children. Accuracy reported here with multivariate analysis combined with Monte Carlo cross-validation is higher than in previous studies. Our findings demonstrate that easy-to-measure voice acoustic parameters could be used as a diagnostic aid tool, specific to ASD.https://doi.org/10.1038/s41398-023-02554-8 |
| spellingShingle | Frédéric Briend Céline David Silvia Silleresi Joëlle Malvy Sandrine Ferré Marianne Latinus Voice acoustics allow classifying autism spectrum disorder with high accuracy Translational Psychiatry |
| title | Voice acoustics allow classifying autism spectrum disorder with high accuracy |
| title_full | Voice acoustics allow classifying autism spectrum disorder with high accuracy |
| title_fullStr | Voice acoustics allow classifying autism spectrum disorder with high accuracy |
| title_full_unstemmed | Voice acoustics allow classifying autism spectrum disorder with high accuracy |
| title_short | Voice acoustics allow classifying autism spectrum disorder with high accuracy |
| title_sort | voice acoustics allow classifying autism spectrum disorder with high accuracy |
| url | https://doi.org/10.1038/s41398-023-02554-8 |
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