Feasible Human Emotion Detection from Facial Thermal Images

Recognition of Human Emotion from a Speech Signal Based on Plutchik's ModelMachine recognition of human emotional states is an essential part in improving man-machine interaction. During expressive speech the voice conveys semantic message as well as the information about emotional state of the speaker. The pitch contour is one of the most significant properties of speech, which is affected by the emotional state. Therefore pitch features have been commonly used in systems for automatic emotion detection. In this work different intensities of emotions and their influence on pitch features have been studied. This understanding is important to develop such a system. Intensities of emotions are presented on Plutchik's cone-shaped 3D model. ThekNearest Neighbor algorithm has been used for classification. The classification has been divided into two parts. First, the primary emotion has been detected, then its intensity has been specified. The results show that the recognition accuracy of the system is over 50% for primary emotions, and over 70% for its intensities.

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Analyzing the Effect of Hyper-Parameters on a Neural Network for Emotion Detection

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1206 ◽

2021 ◽

pp. 33-40

Author(s):

Ambrish Jhamnani ◽

Anshika Tiwari ◽

Abhishek Soni ◽

Arpit Deo

Keyword(s):

Neural Network ◽

Prediction Model ◽

Optimal Solution ◽

Poor Performance ◽

Activation Function ◽

Human Face ◽

Emotion Detection ◽

Good Balance ◽

Human Emotion ◽

The Poor

Human emotion prediction is a tough task. The human face is extremely complex to understand. To build an optimal solution for human emotion prediction model, setting hyper-parameter plays a major role. It is a difficult task to train a neural network. The poor performance of the model can result from poor judgment of sub-optimal hyper- parameters before training the model. This study aims to compare different hyper-parameters and their effect to train the convolutional neural network for emotion detection. We used different methods based on values of validation accuracy and validation loss. The study reveals that SELU activation function performs better in terms of validation accuracy. Swish activation function maintains a good balance between validation accuracy and validation loss. As different combinations of parameters behave differently likewise in optimizers, RMS prop gives less validation loss with Swish whereas Adam performs better with ReLU and ELU activation function.

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Deep learning approach to text analysis for human emotion detection from big data

Journal of Intelligent Systems ◽

10.1515/jisys-2022-0001 ◽

2022 ◽

Vol 31 (1) ◽

pp. 113-126

Author(s):

Jia Guo

Keyword(s):

Big Data ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Text Analysis ◽

Question Answering ◽

Word Embeddings ◽

Emotion Detection ◽

Human Emotion

Abstract Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing (NLP) and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis (DLSTA) has been proposed for human emotion detection using big data. Emotion detection from textual sources can be done utilizing notions of Natural Language Processing. Word embeddings are extensively utilized for several NLP tasks, like machine translation, sentiment analysis, and question answering. NLP techniques improve the performance of learning-based methods by incorporating the semantic and syntactic features of the text. The numerical outcomes demonstrate that the suggested method achieves an expressively superior quality of human emotion detection rate of 97.22% and the classification accuracy rate of 98.02% with different state-of-the-art methods and can be enhanced by other emotional word embeddings.

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Machine Learning in Human Emotion Detection from the Speech

Journal of Interconnection Networks ◽

10.1142/s0219265921460038 ◽

2021 ◽

Author(s):

Xiaoli Qiu ◽

Wei Li ◽

Yang Li ◽

Hongmei Gu ◽

Fei Song ◽

...

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Pulse Power ◽

Speech Emotion Recognition ◽

Learning Science ◽

Emotion Detection ◽

Classification Techniques ◽

Human Emotion ◽

Research Findings

The identification of speech emotions is amongst the most strenuous and fascinating fields of machine learning science. In this article, Chinese emotions are classified as a disruptive atmosphere that classifies several feelings into four major emotional organizations: pleasure, sorrow, resentment, and neutrality. A machine learning in human emotion detection (ML-HED) framework is proposed. The technology suggested removing prosodic and spectrum elements of an audio wave, such as a pulse, power, amplitude, Cepstrum melt frequency correlations, linearly fixed Cepstral, and identification with a template. In all, 87,75% of performers’ statements and 93% of women’s actors were given reliability. The research findings show that the revolutionary technology achieves greater precision by accurately interpreting the feelings, which contrasts with current speech emotion recognition approaches. Besides, the derived characteristics were contrasting with various classification techniques in this study for the comprehensive idea.

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