The Next Generation of Edutainment Applications for Young Children—A Proposal

Guran, Adriana-Mihaela; Cojocar, Grigoreta Sofia; Dioşan, Laura

doi:10.3390/math10040645

Cited by 6 publications

(2 citation statements)

References 25 publications

(23 reference statements)

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“…Second, a comparison with the results of validation of other databases of children's emotional speech on the same and other classifiers showed that our database contains reliable samples of children's emotional speech which can be used to develop various edutainment [76], health care, etc. applications using different types of classifiers.…”

Section: Discussionmentioning

confidence: 95%

Automatic Speech Emotion Recognition of Younger School Age Children

Matveev¹,

Matveev²,

Frolova³

et al. 2022

Mathematics

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This paper introduces the extended description of a database that contains emotional speech in the Russian language of younger school age (8–12-year-old) children and describes the results of validation of the database based on classical machine learning algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). The validation is performed using standard procedures and scenarios of the validation similar to other well-known databases of children’s emotional acting speech. Performance evaluation of automatic multiclass recognition on four emotion classes “Neutral (Calm)—Joy—Sadness—Anger” shows the superiority of SVM performance and also MLP performance over the results of perceptual tests. Moreover, the results of automatic recognition on the test dataset which was used in the perceptual test are even better. These results prove that emotions in the database can be reliably recognized both by experts and automatically using classical machine learning algorithms such as SVM and MLP, which can be used as baselines for comparing emotion recognition systems based on more sophisticated modern machine learning methods and deep neural networks. The results also confirm that this database can be a valuable resource for researchers studying affective reactions in speech communication during child-computer interactions in the Russian language and can be used to develop various edutainment, health care, etc. applications.

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Section: Discussionmentioning

confidence: 95%

Automatic Speech Emotion Recognition of Younger School Age Children

Matveev¹,

Matveev²,

Frolova³

et al. 2022

Mathematics

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“…That is because large corpora of children's speech, especially audio-visual speech, are still not publicly available, and this forces researchers to focus on emotion recognition in adult speech. Nevertheless, children are potentially the largest class of users of most HCI applications, especially in education and entertainment (edutainment) [5]. Therefore, it is important to understand how emotions are expressed by children and whether they can be automatically recognized.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition

Matveev,

Frolova

et al. 2023

Mathematics

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Detecting and understanding emotions are critical for our daily activities. As emotion recognition (ER) systems develop, we start looking at more difficult cases than just acted adult audio–visual speech. In this work, we investigate the automatic classification of the audio–visual emotional speech of children, which presents several challenges including the lack of publicly available annotated datasets and the low performance of the state-of-the art audio–visual ER systems. In this paper, we present a new corpus of children’s audio–visual emotional speech that we collected. Then, we propose a neural network solution that improves the utilization of the temporal relationships between audio and video modalities in the cross-modal fusion for children’s audio–visual emotion recognition. We select a state-of-the-art neural network architecture as a baseline and present several modifications focused on a deeper learning of the cross-modal temporal relationships using attention. By conducting experiments with our proposed approach and the selected baseline model, we observe a relative improvement in performance by 2%. Finally, we conclude that focusing more on the cross-modal temporal relationships may be beneficial for building ER systems for child–machine communications and environments where qualified professionals work with children.

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