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Corrales Astorgano, Mario González Ferreras, César Escudero Mancebo, David Cardeñoso Payo, Valentín 2026-01-23T09:56:39Z 2026-01-23T09:56:39Z 2024 Applied Sciences, January 2024, vol. 14, n. 1, p. 1-13. 2076-3417 https://uvadoc.uva.es/handle/10324/82062 10.3390/app14010293 1 1 13 Applied Sciences 14 2076-3417 Evaluating prosodic quality poses unique challenges due to the intricate nature of prosody, which encompasses multiple form–function profiles. These challenges are more pronounced when analyzing the voices of individuals with Down syndrome (DS) due to increased variability. This paper introduces a procedure for selecting informative prosodic features based on both the disparity between human-rated DS productions and their divergence from the productions of typical users, utilizing a corpus constructed through a video game. Individual reports of five speakers with DS are created by comparing the selected features of each user with recordings of individuals without intellectual disabilities. The acquired features primarily relate to the temporal domain, reducing dependence on pitch detection algorithms, which encounter difficulties when dealing with pathological voices compared to typical ones. These individual reports can be instrumental in identifying specific issues for each speaker, assisting therapists in defining tailored training sessions based on the speaker’s profile. This work was carried out in the Project PID2021-126315OB-I00 that was supported by MCIN/AEI/10.13039/501100011033/FEDER,EU. application/pdf eng MDPI info:eu-repo/semantics/openAccess Down syndrome; automatic classification; prosody Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/2076-3417/14/1/293 SI