Automatic Voice Classification Of Autistic Subjects
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915128656527360 |
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| author | Vacca, Jessica Brondino, Natascia Dell'Acqua, Fabio Vizziello, Anna Savazzi, Pietro |
| author_facet | Vacca, Jessica Brondino, Natascia Dell'Acqua, Fabio Vizziello, Anna Savazzi, Pietro |
| contents | Autism Spectrum Disorders (ASD) describe a heterogeneous set of conditions classified as neurodevelopmental disorders. Although the mechanisms underlying ASD are not yet fully understood, more recent literature focused on multiple genetics and/or environmental risk factors. Heterogeneity of symptoms, especially in milder forms of this condition, could be a challenge for the clinician. In this work, an automatic speech classification algorithm is proposed to characterize the prosodic elements that best distinguish autism, to support the traditional diagnosis. The performance of the proposed algorithm is evaluted by testing the classification algorithms on a dataset composed of recorded speeches, collected among both autustic and non autistic subjects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Voice Classification Of Autistic Subjects Vacca, Jessica Brondino, Natascia Dell'Acqua, Fabio Vizziello, Anna Savazzi, Pietro Signal Processing Sound Audio and Speech Processing Autism Spectrum Disorders (ASD) describe a heterogeneous set of conditions classified as neurodevelopmental disorders. Although the mechanisms underlying ASD are not yet fully understood, more recent literature focused on multiple genetics and/or environmental risk factors. Heterogeneity of symptoms, especially in milder forms of this condition, could be a challenge for the clinician. In this work, an automatic speech classification algorithm is proposed to characterize the prosodic elements that best distinguish autism, to support the traditional diagnosis. The performance of the proposed algorithm is evaluted by testing the classification algorithms on a dataset composed of recorded speeches, collected among both autustic and non autistic subjects. |
| title | Automatic Voice Classification Of Autistic Subjects |
| topic | Signal Processing Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.13470 |