Standard
Assessment of Children’s Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale. / Ляксо, Елена Евгеньевна; Фролова, Ольга Владимировна; Матвеев, Антон Юрьевич; Николаев, Александр Сергеевич; Ruban, Nersisson.
26th International Conference, SPECOM 2024, Belgrade, Serbia, November 25–28, 2024, Proceedings, Part I. ed. / Alexey Karpov; Vlado Delić. 2024. p. 281–294 (Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence); Vol. LNAI 15299).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Ляксо, ЕЕ, Фролова, ОВ, Матвеев, АЮ
, Николаев, АС & Ruban, N 2024,
Assessment of Children’s Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale. in A Karpov & V Delić (eds),
26th International Conference, SPECOM 2024, Belgrade, Serbia, November 25–28, 2024, Proceedings, Part I. Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence), vol. LNAI 15299, pp. 281–294, 26th International Conference on Speech and Computer , Белград, Serbia,
25/11/24.
https://doi.org/10.1007/978-3-031-77961-9_21
APA
Ляксо, Е. Е., Фролова, О. В., Матвеев, А. Ю.
, Николаев, А. С., & Ruban, N. (2024).
Assessment of Children’s Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale. In A. Karpov, & V. Delić (Eds.),
26th International Conference, SPECOM 2024, Belgrade, Serbia, November 25–28, 2024, Proceedings, Part I (pp. 281–294). (Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence); Vol. LNAI 15299).
https://doi.org/10.1007/978-3-031-77961-9_21
Vancouver
Ляксо ЕЕ, Фролова ОВ, Матвеев АЮ
, Николаев АС, Ruban N.
Assessment of Children’s Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale. In Karpov A, Delić V, editors, 26th International Conference, SPECOM 2024, Belgrade, Serbia, November 25–28, 2024, Proceedings, Part I. 2024. p. 281–294. (Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence)).
https://doi.org/10.1007/978-3-031-77961-9_21
Author
Ляксо, Елена Евгеньевна ; Фролова, Ольга Владимировна ; Матвеев, Антон Юрьевич
; Николаев, Александр Сергеевич ; Ruban, Nersisson. /
Assessment of Children’s Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale. 26th International Conference, SPECOM 2024, Belgrade, Serbia, November 25–28, 2024, Proceedings, Part I. editor / Alexey Karpov ; Vlado Delić. 2024. pp. 281–294 (Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence)).
BibTeX
@inproceedings{5ea4085c6d304a7bbdf612933768d751,
title = "Assessment of Children{\textquoteright}s Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale",
abstract = "The goal of the study was to assess children{\textquoteright}s ability to manifest emotions in facial expressions and speech by humans, automatic and using Likert scale scores. To achieve this goal, two studies were conducted. The first study performed a perceptual and automatic analysis of the emotions “joy - neutral - sadness - anger” in typically developing (TD) children; the second - compared emotion recognition in four groups of children - TD, autism spectrum disorders (ASD), intellectual disabilities (ID) and Down syndrome (DS) by expert and automatic, and analyzed Likert scale scores for completing test tasks. The participants of the study were 110 children aged 5 - 16 years, 18 adults. The original dataset containing video and audio fragments of children{\textquoteright}s emotional states was used. Experts recognize the emotions of children from all groups by video and speech more accurately than automatic classifications, with higher UAR values for TD children by audio and video in a perceptual experiment and by audio in the automatic classification of emotions. Differences in the classification accuracy of emotions in children with ASD, ID, and DS were identified. Sadness and anger states are automatically classified poorly by audio and video in children with ASD, ID, and DS. The novelty of the results lays in the obtaining normative data on the recognition of emotions in TD children and in comparative data on groups of TD children, children with ASD, ID, DS.",
keywords = "Emotional State, Perceptual and Automatic Recognition ·Children, Video, Audio Modalities, Likert Scale Scores, Perceptual and Automatic Recognition, Children",
author = "Ляксо, {Елена Евгеньевна} and Фролова, {Ольга Владимировна} and Матвеев, {Антон Юрьевич} and Николаев, {Александр Сергеевич} and Nersisson Ruban",
note = "This study is financially supported by the Russian Science Foundation (project 22–45-02007) - for Russian researches, DST/INT/RUS/RSF/P-57/2021 – for Indian researches.; 26th International Conference on Speech and Computer , SPECOM 2024 ; Conference date: 25-11-2024 Through 28-11-2024",
year = "2024",
month = nov,
day = "25",
doi = "10.1007/978-3-031-77961-9_21",
language = "English",
isbn = "978-3-031-77960-2",
series = "Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence)",
publisher = "Springer Nature Switzerland",
pages = "281–294",
editor = "Alexey Karpov and Vlado Deli{\'c}",
booktitle = "26th International Conference, SPECOM 2024, Belgrade, Serbia, November 25–28, 2024, Proceedings, Part I",
url = "https://specom.nw.ru/2024/, https://specom2024.ftn.uns.ac.rs, https://specom2024.ftn.uns.ac.rs/",
}
RIS
TY - GEN
T1 - Assessment of Children’s Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale
AU - Ляксо, Елена Евгеньевна
AU - Фролова, Ольга Владимировна
AU - Матвеев, Антон Юрьевич
AU - Николаев, Александр Сергеевич
AU - Ruban, Nersisson
N1 - Conference code: 26
PY - 2024/11/25
Y1 - 2024/11/25
N2 - The goal of the study was to assess children’s ability to manifest emotions in facial expressions and speech by humans, automatic and using Likert scale scores. To achieve this goal, two studies were conducted. The first study performed a perceptual and automatic analysis of the emotions “joy - neutral - sadness - anger” in typically developing (TD) children; the second - compared emotion recognition in four groups of children - TD, autism spectrum disorders (ASD), intellectual disabilities (ID) and Down syndrome (DS) by expert and automatic, and analyzed Likert scale scores for completing test tasks. The participants of the study were 110 children aged 5 - 16 years, 18 adults. The original dataset containing video and audio fragments of children’s emotional states was used. Experts recognize the emotions of children from all groups by video and speech more accurately than automatic classifications, with higher UAR values for TD children by audio and video in a perceptual experiment and by audio in the automatic classification of emotions. Differences in the classification accuracy of emotions in children with ASD, ID, and DS were identified. Sadness and anger states are automatically classified poorly by audio and video in children with ASD, ID, and DS. The novelty of the results lays in the obtaining normative data on the recognition of emotions in TD children and in comparative data on groups of TD children, children with ASD, ID, DS.
AB - The goal of the study was to assess children’s ability to manifest emotions in facial expressions and speech by humans, automatic and using Likert scale scores. To achieve this goal, two studies were conducted. The first study performed a perceptual and automatic analysis of the emotions “joy - neutral - sadness - anger” in typically developing (TD) children; the second - compared emotion recognition in four groups of children - TD, autism spectrum disorders (ASD), intellectual disabilities (ID) and Down syndrome (DS) by expert and automatic, and analyzed Likert scale scores for completing test tasks. The participants of the study were 110 children aged 5 - 16 years, 18 adults. The original dataset containing video and audio fragments of children’s emotional states was used. Experts recognize the emotions of children from all groups by video and speech more accurately than automatic classifications, with higher UAR values for TD children by audio and video in a perceptual experiment and by audio in the automatic classification of emotions. Differences in the classification accuracy of emotions in children with ASD, ID, and DS were identified. Sadness and anger states are automatically classified poorly by audio and video in children with ASD, ID, and DS. The novelty of the results lays in the obtaining normative data on the recognition of emotions in TD children and in comparative data on groups of TD children, children with ASD, ID, DS.
KW - Emotional State
KW - Perceptual and Automatic Recognition ·Children
KW - Video
KW - Audio Modalities
KW - Likert Scale Scores
KW - Perceptual and Automatic Recognition
KW - Children
UR - https://www.mendeley.com/catalogue/2b2c9fda-1d1b-3b19-b539-1a9ac9448690/
U2 - 10.1007/978-3-031-77961-9_21
DO - 10.1007/978-3-031-77961-9_21
M3 - Conference contribution
SN - 978-3-031-77960-2
T3 - Lecture Notes in Computer Science ( Lecture Notes in Artificial Intelligence)
SP - 281
EP - 294
BT - 26th International Conference, SPECOM 2024, Belgrade, Serbia, November 25–28, 2024, Proceedings, Part I
A2 - Karpov, Alexey
A2 - Delić, Vlado
T2 - 26th International Conference on Speech and Computer
Y2 - 25 November 2024 through 28 November 2024
ER -