A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions

Matin, Rezwan; Valles, Damian

doi:10.1109/ietc47856.2020.9249147

Cited by 24 publications

(15 citation statements)

References 16 publications

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“…Table 4 compares the results of speech emotion recognition in previous studies [ 24 , 33 , 34 , 35 ] and this study. Sun, T.W [ 24 ] proposed a novel emotion recognition algorithm that does not rely on any speech acoustic features and combines speaker gender information with the emerging R-CNN structure.…”

Section: Discussionmentioning

confidence: 84%

A Deep Learning Method Using Gender-Specific Features for Emotion Recognition

Zhang

et al. 2023

Sensors

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Speech reflects people’s mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition models.

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Section: Discussionmentioning

confidence: 84%

A Deep Learning Method Using Gender-Specific Features for Emotion Recognition

Zhang

et al. 2023

Sensors

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“…Most of the corpora are in the English language, but some of them are in the German, French, Spanish, Mandarin, Sesotho, Filipino, and Russian languages. Moreover, these corpora vary not only by language but also by children's age range, different specific groups like specific language impairment [30], autism spectrum disorder [31], etc.…”

Section: Children's Emotion Speech Corporamentioning

confidence: 99%

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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“…Nowadays, human natural language emotion recognition has been widely applied to various aspects of human life. For example, in criminal investigation, lie detectors detect the psychological state of suspects through emotional recognition [1]; In terms of safety protection, the on-board SER ensures the safety of both the driver and passengers by identifying the driver's psychological state; There is also an automatic remote service center for human-computer interaction [1], used to collect user feedback information; In terms of education, emotional recognition techniques and medical aspects that assist teachers in exploring students' psychological states can help children with autism spectrum disorder (ASD) recognize emotions in social interactions through emotional recognition [5]; There are also home robots in the family, which can perform simple operations such as cleaning and transporting items through the owner's language and emotions [1].…”

Section: Introductionmentioning

confidence: 99%

“…These seven different articles all emphasize the importance of studying how computers recognize human natural language emotions, as well as the significant changes it can bring to various fields. The aspects of natural language emotion recognition they explored are quantifiable recognition of language signal sensors, enabling them to be applied to different aspects such as human restart interaction, virtual reality, behavior evaluation, healthcare, and emergency call centers; Propose an end-to-end model consisting of a convolutional neural network (CNN) for recognizing natural language emotions based on existing deep neural networks (DNNs) [3]; Research on the recognition accuracy of the inherent time relationship of speech waveforms, and a new language recognition method proposed to address the issue of insufficient recognition accuracy [2]; Optimize the phase packaging of Mel frequency spectrum coefficients (MFCCs) and Mel spectrograms to solve problems such as signal processing difficulties and phase information being ignored [7]; Solve the problem of difficulty in conveniently detecting human hidden emotions by establishing a single, standardized formula due to the diversity and complexity of human language [6]; There is also the function of natural language emotion recognition to help children with autism spectrum disorder (ASD) recognize emotions in social interactions and help them overcome the harm caused by ASD [5]; And by building a platform for intelligent home robots, they can achieve simple operations such as cleaning and hygiene by recognizing the natural language of their owners [1].…”

Section: Introductionmentioning

confidence: 99%

Advancements and challenges in speech emotion recognition: a comprehensive review

Wang,

Yin,

Zhou

et al. 2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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As the importance of human-computer interaction (HCI) continues to strengthen and the field of deep learning evolves, numerous models have found their application in the realm of Speech Emotion Recognition (SER), leading to significant advancements in recent years. However, effectively recognizing and processing human emotions through computational systems remains a complex and formidable challenge. This review aims to provide a comprehensive summary of the latest accomplishments in SER, encompassing a diverse range of application scenarios, from education and healthcare to criminal investigation. Additionally, it delves into various models and preprocessing techniques such as Convolutional Neural Networks (CNN), Convolutional Recurrent Neural Networks (CRNN), Long Short-Term Memory (LSTM), and datasets like RAVDESS and RECOLA, which encompass a wide array of scenes and languages. While the recent strides in SER have undeniably achieved impressive accuracy rates, a notable gap exists in research that addresses more intricate emotional contexts, including situations involving irony or sarcasm. Consequently, this review focuses on a comprehensive analysis of the limitations inherent in different feature engineering strategies. Moreover, it investigates the challenge of interpretability posed by complex models, the constraint posed by singular and hard-to-gather datasets, and the expansive scope of potential applications SER could serve. Considering these complexities, a potential pathway to further enhance SER's effectiveness and applicability is proposed. This involves exploring the concept of non-binary emotion classification, harnessing rich contextual information, and integrating datasets that incorporate gesture and textual data. By adapting feature extraction techniques to align with the unique demands of specific scenarios, the performance of SER models could be markedly improved.

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A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions

Cited by 24 publications

References 16 publications

A Deep Learning Method Using Gender-Specific Features for Emotion Recognition

A Deep Learning Method Using Gender-Specific Features for Emotion Recognition

Automatic Speech Emotion Recognition of Younger School Age Children

Advancements and challenges in speech emotion recognition: a comprehensive review

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