Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome
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 p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/1/293 |
| _version_ | 1797359081432809472 |
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| author | Mario Corrales-Astorgano César González-Ferreras David Escudero-Mancebo Valentín Cardeñoso-Payo |
| author_facet | Mario Corrales-Astorgano César González-Ferreras David Escudero-Mancebo Valentín Cardeñoso-Payo |
| author_sort | Mario Corrales-Astorgano |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T15:11:41Z |
| format | Article |
| id | doaj.art-8088e6e208754f24afcb3c5d51f900d8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:11:41Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8088e6e208754f24afcb3c5d51f900d82024-01-10T14:51:38ZengMDPI AGApplied Sciences2076-34172023-12-0114129310.3390/app14010293Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down SyndromeMario Corrales-Astorgano0César González-Ferreras1David Escudero-Mancebo2Valentín Cardeñoso-Payo3Research Group ECA-SIMM, Computer Science Department, University of Valladolid, 47002 Valladolid, SpainResearch Group ECA-SIMM, Computer Science Department, University of Valladolid, 47002 Valladolid, SpainResearch Group ECA-SIMM, Computer Science Department, University of Valladolid, 47002 Valladolid, SpainResearch Group ECA-SIMM, Computer Science Department, University of Valladolid, 47002 Valladolid, SpainEvaluating 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.https://www.mdpi.com/2076-3417/14/1/293down syndromeautomatic classificationprosody |
| spellingShingle | Mario Corrales-Astorgano César González-Ferreras David Escudero-Mancebo Valentín Cardeñoso-Payo Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome Applied Sciences down syndrome automatic classification prosody |
| title | Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome |
| title_full | Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome |
| title_fullStr | Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome |
| title_full_unstemmed | Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome |
| title_short | Prosodic Feature Analysis for Automatic Speech Assessment and Individual Report Generation in People with Down Syndrome |
| title_sort | prosodic feature analysis for automatic speech assessment and individual report generation in people with down syndrome |
| topic | down syndrome automatic classification prosody |
| url | https://www.mdpi.com/2076-3417/14/1/293 |
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