Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach
Copyright © 2017 ISCA. Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been char...
| Main Authors: | , , , , , , , , |
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| Format: | Conference Paper |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/62419 |
| _version_ | 1848760847441068032 |
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| author | Pokorny, F. Schuller, B. Marschik, P. Brueckner, R. Nyström, P. Cummins, N. Bolte, Sven Einspieler, C. Falck-Ytter, T. |
| author_facet | Pokorny, F. Schuller, B. Marschik, P. Brueckner, R. Nyström, P. Cummins, N. Bolte, Sven Einspieler, C. Falck-Ytter, T. |
| author_sort | Pokorny, F. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Copyright © 2017 ISCA. Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study, we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD.We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD. |
| first_indexed | 2025-11-14T10:22:17Z |
| format | Conference Paper |
| id | curtin-20.500.11937-62419 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:22:17Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-624192018-02-01T05:58:13Z Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach Pokorny, F. Schuller, B. Marschik, P. Brueckner, R. Nyström, P. Cummins, N. Bolte, Sven Einspieler, C. Falck-Ytter, T. Copyright © 2017 ISCA. Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study, we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD.We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD. 2017 Conference Paper http://hdl.handle.net/20.500.11937/62419 10.21437/Interspeech.2017-1007 restricted |
| spellingShingle | Pokorny, F. Schuller, B. Marschik, P. Brueckner, R. Nyström, P. Cummins, N. Bolte, Sven Einspieler, C. Falck-Ytter, T. Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach |
| title | Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach |
| title_full | Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach |
| title_fullStr | Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach |
| title_full_unstemmed | Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach |
| title_short | Earlier identification of children with autism spectrum disorder: An automatic vocalisation-based approach |
| title_sort | earlier identification of children with autism spectrum disorder: an automatic vocalisation-based approach |
| url | http://hdl.handle.net/20.500.11937/62419 |