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        <title>Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders.</title>
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            <title>Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference</title>
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    <abstract>Autism Spectrum Disorders (ASD) are a group of complex developmental conditions whose effects and severity show high intraindividual variability. However, one of the main symptoms shared along the spectrum is social interaction impairments that can be explored through acoustic analysis of speech production. In this paper, we compare 14 Italian-speaking children with ASD and 14 typically developing peers. Accordingly, we extracted and selected the acoustic features related to prosody, quality of voice, loudness, and spectral distribution using the parameter set eGeMAPS provided by the openSMILE feature extraction toolkit. We implemented four supervised machine learning methods to evaluate the extraction performances. Our findings show that Decision Trees (DTs) and Support Vector Machines (SVMs) are the best-performing methods. The overall DT models reach a 100% recall on all the trials, meaning they correctly recognise autistic features. However, half of its models overfit, while SVMs are more consistent. One of the results of the work is the creation of a speech pipeline to extract Italian speech biomarkers typical of ASD by comparing our results with studies based on other languages. A better understanding of this topic can support clinicians in diagnosing the disorder.</abstract>
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%0 Conference Proceedings
%T Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders.
%A Beccaria, Federica
%A Gagliardi, Gloria
%A Kokkinakis, Dimitrios
%Y Kokkinakis, Dimitrios
%Y Themistocleous, Charalambos K.
%Y Fors, Kristina Lundholm
%Y Tsanas, Athanasios
%Y Fraser, Kathleen C.
%S Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F beccaria-etal-2022-extraction
%X Autism Spectrum Disorders (ASD) are a group of complex developmental conditions whose effects and severity show high intraindividual variability. However, one of the main symptoms shared along the spectrum is social interaction impairments that can be explored through acoustic analysis of speech production. In this paper, we compare 14 Italian-speaking children with ASD and 14 typically developing peers. Accordingly, we extracted and selected the acoustic features related to prosody, quality of voice, loudness, and spectral distribution using the parameter set eGeMAPS provided by the openSMILE feature extraction toolkit. We implemented four supervised machine learning methods to evaluate the extraction performances. Our findings show that Decision Trees (DTs) and Support Vector Machines (SVMs) are the best-performing methods. The overall DT models reach a 100% recall on all the trials, meaning they correctly recognise autistic features. However, half of its models overfit, while SVMs are more consistent. One of the results of the work is the creation of a speech pipeline to extract Italian speech biomarkers typical of ASD by comparing our results with studies based on other languages. A better understanding of this topic can support clinicians in diagnosing the disorder.
%U https://aclanthology.org/2022.rapid-1.4
%P 22-30

Markdown (Informal)

[Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders.](https://aclanthology.org/2022.rapid-1.4) (Beccaria et al., RaPID 2022)

• Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders. (Beccaria et al., RaPID 2022)

ACL

• Federica Beccaria, Gloria Gagliardi, and Dimitrios Kokkinakis. 2022. Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders.. In Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference, pages 22–30, Marseille, France. European Language Resources Association.