Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Attention Deficit Hyperactivity Disorder: Identifying Approaches for Early Diagnosis, a Pilot Study. / Ляксо, Елена Евгеньевна; Фролова, Ольга Владимировна; Матвеев, Антон Юрьевич; Шабанов, Петр Дмитриевич; Лебедев, Андрей Андреевич; Николаев, Александр Сергеевич; Клешнев, Егор Анатольевич; Гречаный, Северин Вячеславович; Ruban, Nersissona.
Speech and Computer 27th International Conference, SPECOM 2025 Szeged, Hungary, October 13–15, 2025 Proceedings, Part I: SPECOM 2025. ред. / Alexey Karpov; Gábor Gosztolya. Том 16187 Switzerland : Springer Nature, 2025. стр. 188-202 (Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence); Том LNAI 16187).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Attention Deficit Hyperactivity Disorder: Identifying Approaches for Early Diagnosis, a Pilot Study.
AU - Ляксо, Елена Евгеньевна
AU - Фролова, Ольга Владимировна
AU - Матвеев, Антон Юрьевич
AU - Шабанов, Петр Дмитриевич
AU - Лебедев, Андрей Андреевич
AU - Николаев, Александр Сергеевич
AU - Клешнев, Егор Анатольевич
AU - Гречаный, Северин Вячеславович
AU - Ruban, Nersissona
N1 - Conference code: 27
PY - 2025/10/13
Y1 - 2025/10/13
N2 - The aim of the study is to identify objective diagnostic criteria for attention deficit hyperactivity disorder (ADHD) based on the analysis of speech and behavioral indicators. The paper presents the pilot data on the analysis of the speech features and behavioral patterns of 92 children aged 5–13 years with ADHD, ADHD with combined disorders, and control groups. We tested children on their ability to complete the test task “co-op play” of the CEDM method. Different types of data analysis were used - instrumental analysis of speech, expert analysis of children’s behavior, assessment of children’s psychoneurological state by their voice and speech by groups of listeners; automatic analysis of facial expression and ML-based automatic classification of diagnoses of children by their speech. Children with ADHD do not differ significantly from typically developing (TD) children in the analyzed speech features, had lower scores for Play and Behavior scales. Children with ADHD + autism spectrum disorders (ASD) have worse speech characteristics - high values of pitch, lower speech activity, lower scores for behavior and play compared to children in other groups. Our experiments with automatic classification showed that ML model is capable of capturing discriminative features in voice of atypically developing children. Binary classification showed good accuracy when comparing data from children with diagnoses and TD children, and lower accuracy when classifying ADHD + ASD and ASD. The paper discusses the results of the study, notes its limitations and its future research.
AB - The aim of the study is to identify objective diagnostic criteria for attention deficit hyperactivity disorder (ADHD) based on the analysis of speech and behavioral indicators. The paper presents the pilot data on the analysis of the speech features and behavioral patterns of 92 children aged 5–13 years with ADHD, ADHD with combined disorders, and control groups. We tested children on their ability to complete the test task “co-op play” of the CEDM method. Different types of data analysis were used - instrumental analysis of speech, expert analysis of children’s behavior, assessment of children’s psychoneurological state by their voice and speech by groups of listeners; automatic analysis of facial expression and ML-based automatic classification of diagnoses of children by their speech. Children with ADHD do not differ significantly from typically developing (TD) children in the analyzed speech features, had lower scores for Play and Behavior scales. Children with ADHD + autism spectrum disorders (ASD) have worse speech characteristics - high values of pitch, lower speech activity, lower scores for behavior and play compared to children in other groups. Our experiments with automatic classification showed that ML model is capable of capturing discriminative features in voice of atypically developing children. Binary classification showed good accuracy when comparing data from children with diagnoses and TD children, and lower accuracy when classifying ADHD + ASD and ASD. The paper discusses the results of the study, notes its limitations and its future research.
KW - Attention Deficit Hyperactivity Disorder
KW - Automatic Classification
KW - Behavior
KW - Expert Analysis
KW - Speech
UR - https://www.mendeley.com/catalogue/a0366d3c-fa76-378b-805d-297b0c827f7c/
U2 - 10.1007/978-3-032-07956-5_13
DO - 10.1007/978-3-032-07956-5_13
M3 - Conference contribution
SN - 978-3-032-07955-8
VL - 16187
T3 - Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence)
SP - 188
EP - 202
BT - Speech and Computer 27th International Conference, SPECOM 2025 Szeged, Hungary, October 13–15, 2025 Proceedings, Part I
A2 - Karpov, Alexey
A2 - Gosztolya, Gábor
PB - Springer Nature
CY - Switzerland
T2 - 27th International Conference on Speech and Computer
Y2 - 13 October 2025 through 14 October 2025
ER -
ID: 142829040