Automatic Identification Of Emotions Research Articles

Emotional Speech Recognition for Marathi Language

A spontaneous mental state, emotion does not result from deliberate effort. There are many different kinds of emotions in a speech. Because it enhances interactions between people and technology, automatic emotion identification from human speech is becoming more common today. Several temporal and spectral features of human speech can be extracted. Several methods can be used to categorise pitch-related traits, Mel Frequency Cepstral Coefficients (MFCCs), and speech formants. This study looks at statistical characteristics, including MFCCs and linear discriminant analysis, which were used to categorise these properties (LDA). This article also describes a database of artificially emotionalized Marathi speech. The data samples were collected from Marathi speeches given by men and women that mimicked the emotions that resulted to Marathi utterances that could be utilised in everyday conversation and are interpreted in all analysed emotions. To identify data samples, three essential categories—happy, sad, and angry—were used. For MFCC and LPC, the training accuracy and testing accuracy are 98, 82 and 85,82 respectively.

Exploration of Emotion Analysis and Personalized Emotion Adjustment Algorithm in English Learning Process

An important component of human existence and behaviour is emotion. The automatic identification of emotions has gained significant importance in the domains of affective computing and human-machine interaction in recent times. Getting pictures of people's facial expressions is one of the simplest and least expensive methods for identifying emotions among the several physiological and kinematic data that may be utilised. Because of variations in anatomy, culture, and environment, developing a generalised, cross-subject model for emotion identification from facial expression remains difficult.This study presents an intelligent classroom that uses e-learning to help persons with autism spectrum condition strengthen their emotional abilities. Monitoring autism child engagement and participation in class while looking for signs of attention is necessary for effective classroom education. Teaching quality is increasingly being determined by the teachers' capacity to assess and evaluate the behaviour of their pupils in the classroom. Modern classroom systems are enhanced with the most recent technologies in educational institutions to make them more interactive, student-centered, and personalised. Even with these tools, it can be challenging for teachers to gauge their pupils' levels of interest and focus. This study makes use of contemporary technology to establish a real-time, intelligent vision-based classroom that can track students' moods, attendance, and levels of focus even when they are wearing face masks. In order to evaluate students' attention or lack thereof in a classroom, we trained AlexNet models that recognise student behaviour, including recognising facial expressions. Another designed to manage the irregular relationship between pupils and activities is the Interaction Students Education Neural Network ( Int_Edu_NN). The Q-matrix is used to firstly determine the pupil variable and the assignment indicator. This network is analyzed using two datasets in terms of various parameters and hence the proposed Int_Edu_NN achieves 99.% and 98% of accuracy.

A Feature Selection Algorithm Based on Differential Evolution for English Speech Emotion Recognition

The automatic identification of emotions from speech holds significance in facilitating interactions between humans and machines. To improve the recognition accuracy of speech emotion, we extract mel-frequency cepstral coefficients (MFCCs) and pitch features from raw signals, and an improved differential evolution (DE) algorithm is utilized for feature selection based on K-nearest neighbor (KNN) and random forest (RF) classifiers. The proposed multivariate DE (MDE) adopts three mutation strategies to solve the slow convergence of the classical DE and maintain population diversity, and employs a jumping method to avoid falling into local traps. The simulations are conducted on four public English speech emotion datasets: eNTERFACE05, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAEE), and Toronto Emotional Speech Set (TESS), and they cover a diverse range of emotions. The MDE algorithm is compared with PSO-assisted biogeography-based optimization (BBO_PSO), DE, and the sine cosine algorithm (SCA) on emotion recognition error, number of selected features, and running time. From the results obtained, MDE obtains the errors of 0.5270, 0.5044, 0.4490, and 0.0420 in eNTERFACE05, RAVDESS, SAVEE, and TESS based on the KNN classifier, and the errors of 0.4721, 0.4264, 0.3283 and 0.0114 based on the RF classifier. The proposed algorithm demonstrates excellent performance in emotion recognition accuracy, and it finds meaningful acoustic features from MFCCs and pitch.

Proposal for the Clustering of Characteristics to Identify Emotions in the Development of a Foreign Language Exam

Automatic emotion identification allows for obtaining information on emotions experienced by an individual during certain activities, which is essential for improving their performance or preparing for similar experiences. This document aims to establish the clusters of variables associated with the identification of emotions when a group of students takes a foreign language exam in Portuguese. Once the data clusters are determined, it is possible to establish the perception of emotions in the students with relevant variables and their respective decision thresholds. This study can later be used to build a model that relates the measured variables and the student’s performance so that strategies can be generated to help the student achieve better results on the test. The results indicate that the clusters and range values of the variables can be obtained to observe changes in the concentration of the students. This preliminary information can be used to design a fuzzy inference system to identify the student’s state of concentration.

Human Sentiment Analysis using Machine Learning & Image Processing

Facial expressions reveal a great deal about a person's feelings. One of the most difficult aspects of interpersonal relationships is effectively reading facial emotions. Automatic emotion identification based on facial expression recognition has become a hot topic in domains including computer science, medicine, and psychology. For improved outcomes, HCI research communities employ an automated face expression recognition system. For the recognition of expressions in both static photos and real-time films, many feature extraction algorithms have been developed. Love, happiness, rage, fear, and sadness are all examples of human emotions. These photographs are quite diverse from one another, but they all show the same human emotion. In this study, we look into the prospect of utilising machine learning to predict an image's emotion. These kind of predictions can be employed in applications such as automatic tag predictions for social media photographs. Websites, as well as a buyer purchasing a product from a store or through a social media platform, can be useful for determining product quality.. Keywords:- Human Sentiment, Emotion, CNN, ten emotions, , RAFT.

Stock market tweets annotated with emotions

As stock trading became a popular topic on Twitter, many researchers have proposed different approaches to make predictions on it, relying on the emotions found in messages. However, detailed studies require a reasonably sized corpus with emotions properly annotated. In this work, we introduce a corpus of tweets in Brazilian Portuguese annotated with emotions. Comprising 4,277 tweets, this is, to the best of our knowledge, the largest annotated corpus available in the stock market domain for this language. Amongst its possible uses, the corpus lends itself to the application of machine learning models for automatic emotion identification, as well as to the study of correlations between emotions and stock price movements.

Identifying Emotion Labels From Psychiatric Social Texts Using a Bi-Directional LSTM-CNN Model

Discussion features in online communities can be effectively used to diagnose depression and allow other users or experts to provide self-help resources to those in need. Automatic emotion identification models can quickly and effectively highlight indicators of emotional stress in the text of such discussions. Such communities also provide patients with important knowledge to help better understand their condition. This study proposes a deep learning framework combining word embeddings, bi-directional long short-term memory (Bi-LSTM), and convolutional neural networks (CNN) to identify emotion labels from psychiatric social texts. The Bi-LSTM is a powerful mechanism for extracting features from sequential data in which a sentence consists of multiple words in a particular sequence. CNN is another powerful feature extractor which can convolute many blocks to capture important features. Our proposed deep learning framework also applies word representation techniques to represent semantic relationships between words. The paper thus combines two powerful feature extraction methods with word embedding to automatically identify indicators of emotional stress. Experimental results show that our proposed framework outperformed other models using traditional feature extraction such as bag-of-words (BOW), latent semantic analysis (LSA), independent component analysis (ICA), and LSA+ICA.

Methodology for Automatic Identification of Emotions in Learning Environments

Methodology for Automatic Identification of Emotions in Learning Environments

Predicting emotions in facial expressions from the annotations in naturally occurring first encounters

This paper deals with the automatic identification of emotions from the manual annotations of the shape and functions of facial expressions in a Danish corpus of video recorded naturally occurring first encounters. More specifically, a support vector classified is trained on the corpus annotations to identify emotions in facial expressions. In the classification experiments, we test to what extent emotions expressed in naturally-occurring conversations can be identified automatically by a classifier trained on the manual annotations of the shape of facial expressions and co-occurring speech tokens. We also investigate the relation between emotions and the communicative functions of facial expressions. Both emotion labels and their values in a three dimensional space are identified. The three dimensions are Pleasure, Arousal and Dominance.The results of our experiments indicate that the classifiers perform well in identifying emotions from the coarse-grained descriptions of facial expressions and co-occurring speech. The communicative functions of facial expressions also contribute to emotion identification. The results are promising because the emotion label list comprises fine grained emotions and affective states in naturally occurring conversations, while the shape features of facial expressions are very coarse grained. The classification results also assess that the annotation scheme combining a discrete and a dimensional description, and the manual annotations produced according to it are reliable and can be used to model and test emotional behaviours in emotional cognitive infocommunicative systems.

TexEmo: Conveying Emotion from Text- The Study

Human Computer Interface for communication is a very powerful and most current area of research because the human world is getting more and more digitize and one wants the digital systems to behave like a human being. This requires the digital systems to imitate the human behavior accurately. Emotion is one aspect of human behavior which plays an important role in human feeling and decision making thus influencing the way people interact in the society. In human computer interaction, the computer interfaces need to recognize the emotion of the end users in order to exhibit a truly intelligent behavior. Human express the emotion in the form their facial expression, through their speech, and by writing text. The automatic identification of emotions in texts is important for applications such as: opinion mining and market analysis, affective computing, natural language interfaces, and elearning environments, including educational games. This paper is mainly focused on to conveying the emotion expressed by a text documents. In simple way I can say that main aim of this study is to classify the emotion expressed by the text, based on pre-defined list of emotion i.e. Anger, Joy, Sad, Fear, Disgust, and Surprise. In order to collect the emotion evoking word, this study paper is mainly enlightened on ISEAR dataset, WPARD and Word-Net Affect dataset. A lot of attention is paid to the normalization of text and expand the knowledge base of emotion word. Uses of Vector Space Model for Information retrieval and classification.

Feature Analysis and Evaluation for Automatic Emotion Identification in Speech

The definition of parameters is a crucial step in the development of a system for identifying emotions in speech. Although there is no agreement on which are the best features for this task, it is generally accepted that prosody carries most of the emotional information. Most works in the field use some kind of prosodic features, often in combination with spectral and voice quality parametrizations. Nevertheless, no systematic study has been done comparing these features. This paper presents the analysis of the characteristics of features derived from prosody, spectral envelope, and voice quality as well as their capability to discriminate emotions. In addition, early fusion and late fusion techniques for combining different information sources are evaluated. The results of this analysis are validated with experimental automatic emotion identification tests. Results suggest that spectral envelope features outperform the prosodic ones. Even when different parametrizations are combined, the late fusion of long-term spectral statistics with short-term spectral envelope parameters provides an accuracy comparable to that obtained when all parametrizations are combined.