Emotion recognition and school violence detection from children speech

Han, Tian; Zhang, Jincheng; Zhu, Zhenfeng; Sun, Guobing; Liang, Y. T.; Ferdinando, Hany; Alasaarela, Esko; Seppänen, Tapio; Yu, Xiaoyang; Yang, Shuchang

doi:10.1186/s13638-018-1253-8

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…School bullying is one of the popular violence around the world affecting children both mentally and physically [69], [70]. Bullied children are at an increased risk [71]; they might escape from schools, physically harm themselves and/or commit crimes [10], [71]. Some applications such as "Stop Bullies", "Campus Safety", "ICE BlackBox", "TipOff", and "Back Off Bully", have been developed for bullying prevention.…”

Section: Bullying Detectionmentioning

confidence: 99%

“…For those applications, a user at a risk event needs to press a key on the smartphone, and then the smartphone will send different information (phones, videos, texts, GPS) to certain receiver(s). Such applications are not reliable as they are human-driven [10], [72]. In addition, children at risk cases might not be able to notify responsible persons or prevent themselves from getting hurt.…”

Section: Bullying Detectionmentioning

confidence: 99%

“…There is no doubt that developing intelligent systems for children is more challenging than adults due to children's growth, development and aging factors which change over time [3]. And this definitely affects the performance of intelligent recognition systems influenced by these traits including face recognition [4]- [6], speech recognition [7]- [10] and physiological signals [9]. It is know that collection of data from children is a daunting task.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

2022

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The COVID-19 pandemic can be attributed as a main factor to accelerate the current digital transformation and to encourage innovation and technological adoption. Consequently, the care provided to our children, one of the significant aspects of life, needs to be adapted with the life's changes. Children are our future and our most precious resources. They need our attention in all life domains including health, education, safety and social interaction. Nowadays, technologies have been incorporated with machine learning and it has been proven that they are more powerful, reliable and profitable. Machine learning methods have been applied by many children-related studies to generate predictive models for different applications. The efficacy of the generated models mainly rely on the constructed databases. This article carries out a comprehensive survey on available children's databases constructed for machine-learningbased solutions with their methodologies, characteristics, challenges, and applications. First, it provides an overview of the available studies and classifies them based on their applications. Next, it defines a set of attributes and evaluates them while also shedding light on their pros and cons. The primary concerns related to collection, development and distribution of children's databases are also discussed. This study can be considered as a guideline for researchers in multidisciplinary fields to construct reliable databases and to develop more advanced techniques.

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Section: Bullying Detectionmentioning

confidence: 99%

Section: Bullying Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

2022

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“…Han et al [13] proposed a consecutive elimination process (CEP) algorithm to detect campus bullying events by recognizing children's emotions. They tested the proposed algorithm with both the Berlin database and a violence experiment database, and obtained average accuracies of 79.05% and 66.13%, respectively.…”

Section: Related Workmentioning

confidence: 99%

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

et al. 2021

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Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

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“…Furthermore, research shows its application in detection of school violence based on children’s speech (Han et al. 2018 ). Lastly, it can improve other artificial intelligence applications like playing customized music based on the emotion of the speaker on call, marketing, and intelligent toys.…”

Section: Introductionmentioning

confidence: 99%

Impact of autoencoder based compact representation on emotion detection from audio

Patel

Mankad

2021

J Ambient Intell Human Comput

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Emotion recognition from speech has its fair share of applications and consequently extensive research has been done over the past few years in this interesting field. However, many of the existing solutions aren’t yet ready for real time applications. In this work, we propose a compact representation of audio using conventional autoencoders for dimensionality reduction, and test the approach on two benchmark publicly available datasets. Such compact and simple classification systems where the computing cost is low and memory is managed efficiently may be more useful for real time application. System is evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS). Three classifiers, namely, support vector machines (SVM), decision tree classifier, and convolutional neural networks (CNN) have been implemented to judge the impact of the approach. The results obtained by attempting classification with Alexnet and Resnet50 are also reported. Observations proved that this introduction of autoencoders indeed can improve the classification accuracy of the emotion in the input audio files. It can be concluded that in emotion recognition from speech, the choice and application of dimensionality reduction of audio features impacts the results that are achieved and therefore, by working on this aspect of the general speech emotion recognition model, it may be possible to make great improvements in the future.

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Emotion recognition and school violence detection from children speech

Cited by 10 publications

References 15 publications

A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

Impact of autoencoder based compact representation on emotion detection from audio

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