Standard

CNN based Emotion Detection in Cross Linguistic Children speech. / Lyakso, Elena ; Frolova, Olga ; Ruban, Nersissona; Mekala A., Mary; Noel, Joseph Raj, Alex ; Matveev, Anton ; Matveev, Yuri.

Proceedings of the 24 th International Congress of Acoustics: A15 SPEECH. Том A15 Gyeongju, 2022. стр. 8-14 ABS-0435.

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаяРецензирование

Harvard

Lyakso, E, Frolova, O, Ruban, N, Mekala A., M, Noel, JRA, Matveev, A & Matveev, Y 2022, CNN based Emotion Detection in Cross Linguistic Children speech. в Proceedings of the 24 th International Congress of Acoustics: A15 SPEECH. Том. A15, ABS-0435, Gyeongju, стр. 8-14, 24 th International Congress of Acoustics. October 24 to 28. 2022 in Gyeongju, Korea , Gyeongju, Корейская Народная Демократическая республика, 24/10/22.

APA

Lyakso, E., Frolova, O., Ruban, N., Mekala A., M., Noel, J. R. A., Matveev, A., & Matveev, Y. (2022). CNN based Emotion Detection in Cross Linguistic Children speech. в Proceedings of the 24 th International Congress of Acoustics: A15 SPEECH (Том A15, стр. 8-14). [ABS-0435].

Vancouver

Lyakso E, Frolova O, Ruban N, Mekala A. M, Noel JRA, Matveev A и пр. CNN based Emotion Detection in Cross Linguistic Children speech. в Proceedings of the 24 th International Congress of Acoustics: A15 SPEECH. Том A15. Gyeongju. 2022. стр. 8-14. ABS-0435

Author

Lyakso, Elena ; Frolova, Olga ; Ruban, Nersissona ; Mekala A., Mary ; Noel, Joseph Raj, Alex ; Matveev, Anton ; Matveev, Yuri. / CNN based Emotion Detection in Cross Linguistic Children speech. Proceedings of the 24 th International Congress of Acoustics: A15 SPEECH. Том A15 Gyeongju, 2022. стр. 8-14

BibTeX

@inproceedings{24c78b9716804178bc217748896e88ed,
title = "CNN based Emotion Detection in Cross Linguistic Children speech",
abstract = "The goal of this cross-linguistic study is to analyze the speech of children in the age group of 8-12 years andautomatic classification of children's emotions from various speech data in Russian and Tamil languages. Astandardized approach for the collection of speech (spontaneous and acting speech - emotional words,phrases, and meaningless texts) was used. Two emotional child speech corpora include the emotional speechof 95 Russian children and 40 Indian children were created. Annotation of the emotional speech was carriedout in four categories “joy - neutral - sadness – anger” by two speech specialists of the same nationality of thechild. The set of 2505 labeled audio files of Russian children and 418 labeled audio files of Tamil childrenwere used. The SVM classifier shows slightly better results in Russian language, and the MLP classifier inTamil. Inter-Cultural approach (mixed dataset of Russian and Indian speech) revealed that the accuracy ofrecognition of all emotions remains higher. In Cross-Cultural approach, on samples of Tamil speech, angerstate is recognized better, and in samples of Russian speech – sad state. Using 5 Layered CNN Model, theaccuracy was revealed for Russian speech is higher than for Indian children.",
keywords = "child speech, Emotion Detection, Cross-Linguistic Study",
author = "Elena Lyakso and Olga Frolova and Nersissona Ruban and {Mekala A.}, Mary and Noel, {Joseph Raj, Alex} and Anton Matveev and Yuri Matveev",
year = "2022",
language = "English",
volume = "A15",
pages = "8--14",
booktitle = "Proceedings of the 24 th International Congress of Acoustics",
note = "null ; Conference date: 24-10-2022 Through 28-11-2022",
url = "https://ica2022korea.org/",

}

RIS

TY - GEN

T1 - CNN based Emotion Detection in Cross Linguistic Children speech

AU - Lyakso, Elena

AU - Frolova, Olga

AU - Ruban, Nersissona

AU - Mekala A., Mary

AU - Noel, Joseph Raj, Alex

AU - Matveev, Anton

AU - Matveev, Yuri

PY - 2022

Y1 - 2022

N2 - The goal of this cross-linguistic study is to analyze the speech of children in the age group of 8-12 years andautomatic classification of children's emotions from various speech data in Russian and Tamil languages. Astandardized approach for the collection of speech (spontaneous and acting speech - emotional words,phrases, and meaningless texts) was used. Two emotional child speech corpora include the emotional speechof 95 Russian children and 40 Indian children were created. Annotation of the emotional speech was carriedout in four categories “joy - neutral - sadness – anger” by two speech specialists of the same nationality of thechild. The set of 2505 labeled audio files of Russian children and 418 labeled audio files of Tamil childrenwere used. The SVM classifier shows slightly better results in Russian language, and the MLP classifier inTamil. Inter-Cultural approach (mixed dataset of Russian and Indian speech) revealed that the accuracy ofrecognition of all emotions remains higher. In Cross-Cultural approach, on samples of Tamil speech, angerstate is recognized better, and in samples of Russian speech – sad state. Using 5 Layered CNN Model, theaccuracy was revealed for Russian speech is higher than for Indian children.

AB - The goal of this cross-linguistic study is to analyze the speech of children in the age group of 8-12 years andautomatic classification of children's emotions from various speech data in Russian and Tamil languages. Astandardized approach for the collection of speech (spontaneous and acting speech - emotional words,phrases, and meaningless texts) was used. Two emotional child speech corpora include the emotional speechof 95 Russian children and 40 Indian children were created. Annotation of the emotional speech was carriedout in four categories “joy - neutral - sadness – anger” by two speech specialists of the same nationality of thechild. The set of 2505 labeled audio files of Russian children and 418 labeled audio files of Tamil childrenwere used. The SVM classifier shows slightly better results in Russian language, and the MLP classifier inTamil. Inter-Cultural approach (mixed dataset of Russian and Indian speech) revealed that the accuracy ofrecognition of all emotions remains higher. In Cross-Cultural approach, on samples of Tamil speech, angerstate is recognized better, and in samples of Russian speech – sad state. Using 5 Layered CNN Model, theaccuracy was revealed for Russian speech is higher than for Indian children.

KW - child speech

KW - Emotion Detection

KW - Cross-Linguistic Study

UR - https://www.ica2022korea.org/data/Proceedings_A15.pdf

M3 - Conference contribution

VL - A15

SP - 8

EP - 14

BT - Proceedings of the 24 th International Congress of Acoustics

CY - Gyeongju

Y2 - 24 October 2022 through 28 November 2022

ER -

ID: 100861799