TY  - GEN
N2  - The presence of prosodic anomalies in autistic children is recognized by experienced clinicians but their quantitative analysis is a cumbersome task beyond the scope of typical pen and pencil assessment. Quantifiable speech measurements, i.e., loudness and pitch, are correlated to prosody making them potential references for assisting the analysis. However, their importance is underestimated in the application of autism diagnosis. These measurements are largely ignored in mainstream autism diagnosis batteries and commonly replaced with few vague assessments. This research work emphasizes the potential of speech measurements on autism diagnosis through creating and evaluating a fully automatic speech-oriented autism detecting system.  We propose an automatic approach allowing to tease apart various aspects of prosodic abnormalities and to translate them into fine-grained, automated, and quantifiable measurements.
DO  - 10.6083/n583xv73k
DO  - DOI
AB  - The presence of prosodic anomalies in autistic children is recognized by experienced clinicians but their quantitative analysis is a cumbersome task beyond the scope of typical pen and pencil assessment. Quantifiable speech measurements, i.e., loudness and pitch, are correlated to prosody making them potential references for assisting the analysis. However, their importance is underestimated in the application of autism diagnosis. These measurements are largely ignored in mainstream autism diagnosis batteries and commonly replaced with few vague assessments. This research work emphasizes the potential of speech measurements on autism diagnosis through creating and evaluating a fully automatic speech-oriented autism detecting system.  We propose an automatic approach allowing to tease apart various aspects of prosodic abnormalities and to translate them into fine-grained, automated, and quantifiable measurements.
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
T1  - Quantifying speech characteristics for detecting autism
DA  - 2021
AU  - Chen, Liu
AU  - Asgari, Meysam
AU  - Fombonne, Eric
L1  - https://digitalcollections.ohsu.edu/record/9201/files/Chen-Liu-OHSU-ResearchWeek-2021.pdf
PB  - Oregon Health and Science University
PY  - 2021
ID  - 9201
L4  - https://digitalcollections.ohsu.edu/record/9201/files/Chen-Liu-OHSU-ResearchWeek-2021.pdf
KW  - Early Diagnosis
KW  - Speech Production Measurement
KW  - Machine Learning
KW  - Autistic Disorder
KW  - Child, Preschool
KW  - neurodivergent
KW  - harmonic model
KW  - speech analysis
KW  - prosody
TI  - Quantifying speech characteristics for detecting autism
Y1  - 2021
L2  - https://digitalcollections.ohsu.edu/record/9201/files/Chen-Liu-OHSU-ResearchWeek-2021.pdf
LK  - https://digitalcollections.ohsu.edu/record/9201/files/Chen-Liu-OHSU-ResearchWeek-2021.pdf
UR  - https://digitalcollections.ohsu.edu/record/9201/files/Chen-Liu-OHSU-ResearchWeek-2021.pdf
ER  -