scholarly journals Emotion Recognition using Fuzzy K-Means from Oriya Speech

2011 ◽  
pp. 24-28
Author(s):  
Sanghamitra Mohanty ◽  
Basanta Kumar Swain
Keyword(s):  
Emotion Recognition ◽  
Vocal Communication ◽  
Call Centers ◽  
Research Work ◽  
Research Paper ◽  
Human Communication ◽  
Human Machine Interaction ◽  
Tutoring Systems ◽  
Feature Vectors ◽  
Machine Interaction

Communication will be intelligible when conveyed message is interpreted in right-minded. Unfortunately, the rightminded interpretation of communicated message is possible for human-human communication but it’s laborious for humanmachine communication. It is due to the inherently blending of non-verbal contents such as emotion in vocal communication which leads to difficulty in human-machine interaction. In this research paper we have performed experiment to recognize emotions like anger, sadness, astonish, fear, happiness and neutral using fuzzy K-Means algorithm from Oriya elicited speech collected from 35 Oriya speaking people aged between 22- 58 years belonging to different provinces of Orissa. We have achieved the accuracy of 65.16% in recognizing above six mentioned emotions by incorporating mean pitch, first two formants, jitter, shimmer and energy as feature vectors for this research work. Emotion recognition has many vivid applications in different domains like call centers, spoken tutoring systems, spoken dialogue research, human-robotic interfaces etc.

scholarly journals The emotional state through visual expression, auditory expression and physiological representation

2021 ◽  
Vol 119 ◽  
pp. 05008
Author(s):  
Benyoussef Abdellaoui ◽  
Aniss Moumen ◽  
Younes El Bouzekri El Idrissi ◽  
Ahmed Remaida
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Emotional State ◽  
Emotional Content ◽  
Human Behaviour ◽  
Physiological Signals ◽  
Human Communication ◽  
Human Machine Interaction ◽  
Visual Expression ◽  
Machine Interaction

As emotional content reflects human behaviour, automatic emotion recognition is a topic of growing interest. During the communication of an emotional message, the use of physiological signals and facial expressions gives several advantages that can be expected to understand a person’s personality and psychopathology better and determine human communication and human-machine interaction. In this article, we will present some notions about identifying the emotional state through visual expression, auditory expression and physiological representation, and the techniques used to measure emotions.

Facial Expression Recognition for HCI Applications

2011 ◽  
pp. 625-631 ◽  
Author(s):  
Fadi Dornaika ◽  
Bogdan Raducanu
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Hidden Markov ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Active Appearance Models ◽  
Feature Vectors ◽  
Auto Regressive ◽  
Appearance Models ◽  
Machine Interaction

Facial expression plays an important role in cognition of human emotions (Fasel, 2003 & Yeasin, 2006). The recognition of facial expressions in image sequences with significant head movement is a challenging problem. It is required by many applications such as human-computer interaction and computer graphics animation (Cañamero, 2005 & Picard, 2001). To classify expressions in still images many techniques have been proposed such as Neural Nets (Tian, 2001), Gabor wavelets (Bartlett, 2004), and active appearance models (Sung, 2006). Recently, more attention has been given to modeling facial deformation in dynamic scenarios. Still image classifiers use feature vectors related to a single frame to perform classification. Temporal classifiers try to capture the temporal pattern in the sequence of feature vectors related to each frame such as the Hidden Markov Model based methods (Cohen, 2003, Black, 1997 & Rabiner, 1989) and Dynamic Bayesian Networks (Zhang, 2005). The main contributions of the paper are as follows. First, we propose an efficient recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of a robot whose response is generated according to the user’s recognized facial expression. Our proposed approach has several advantages. First, unlike most expression recognition systems that require a frontal view of the face, our system is viewand texture-independent. Second, its learning phase is simple compared to other techniques (e.g., the Hidden Markov Models and Active Appearance Models), that is, we only need to fit second-order Auto-Regressive models to sequences of facial actions. As a result, even when the imaging conditions change the learned Auto-Regressive models need not to be recomputed. The rest of the paper is organized as follows. Section 2 summarizes our developed appearance-based 3D face tracker that we use to track the 3D head pose as well as the facial actions. Section 3 describes the proposed facial expression recognition based on the detection of keyframes. Section 4 provides some experimental results. Section 5 describes the proposed human-machine interaction application that is based on the developed facial expression recognition scheme.

scholarly journals LOVE END POBOTS. Will humanity become digisexual?

2019 ◽  
Vol 23 (3) ◽  
pp. 312-323
Author(s):  
F G Maylenova
Keyword(s):  
Sex Industry ◽  
Human Communication ◽  
Human Machine Interaction ◽  
New Era ◽  
Private Lives ◽  
Human Love ◽  
Smart Machines ◽  
The One ◽  
The Relationship ◽  
Machine Interaction

Mechanisms that help people in their lives have existed for centuries, and every year they become not only more and more complex and perfect, but also smarter. It is impossible to imagine modern production without smart machines, but today, with the advent of robotic android robots, their participation in our private lives and, consequently, their influence on us, becomes much more obvious. After all, the robots that are increasingly taking root in our lives today, are no longer perceived by us simply as mechanisms, we endow them with human properties of character and often experience different emotions in relation to them. The appearance of a robot capable of experiencing (or still imitating?) emotions can be considered a qualitatively new step in the life of a person with robots. With such robots, it will be possible to be friends with them, to look for (and probably get) support from them. It is expected that they will be able to brighten up the loneliness of a variety of people, including disabled people, lonely old people, to help in caring for the sick and at the same time entertain them with communication. Speaking about the relationship with robots, it is difficult not to mention such an important aspect of human communication as sex, which, on the one hand, is not only a need, as in all living beings, but also the highest form of human love and intimacy, and on the other - can exist and be satisfied completely separate from love. It is this duality of human nature that has contributed to the transformation of sex and the human body into a commodity and the development of prostitution, pornography and the use of sexual images in advertising. The emergence of android robots can radically change our lives, including the most intimate areas of life. The development of the artificial intelligence sex industry opens up a whole new era of human-machine interaction. When smart machines become not only comfortable and entertaining, but literally enter our flesh, become our interlocutors, friends and lovers who share our feelings and interests, what will be the consequences of this unprecedented intimacy between man and machine?

scholarly journals Databases, Features and Classification Techniques for Speech Emotion Recognition

2020 ◽  
Vol 9 (6) ◽  
pp. 185-190
Keyword(s):  
Emotion Recognition ◽  
Research Area ◽  
Research Field ◽  
Speech Emotion Recognition ◽  
Emotional Speech ◽  
Human Machine Interaction ◽  
Classification Techniques ◽  
Classification Feature ◽  
Physical Gestures ◽  
Machine Interaction

Emotion recognition is a rapidly growing research field. Emotions can be effectively expressed through speech and can provide insight about speaker’s intentions. Although, humans can easily interpret emotions through speech, physical gestures, and eye movement but to train a machine to do the same with similar preciseness is quite a challenging task. SER systems can improve human-machine interaction when used with automatic speech recognition, as emotions have the tendency to change the semantics of a sentence. Many researchers have contributed their extremely impressive work in this research area, leading to development of numerous classification, feature selection, feature extraction and emotional speech databases. This paper reviews recent accomplishments in the area of speech emotion recognition. It also present a detailed review of various types of emotional speech databases, and different classification techniques which can be used individually or in combination and a brief description of various speech features for emotion recognition.

Facial Emotion Recognition Using an Ensemble of Multi-Level Convolutional Neural Networks

2019 ◽  
Vol 33 (11) ◽  
pp. 1940015 ◽  
Author(s):  
Hai-Duong Nguyen ◽  
Soonja Yeom ◽  
Guee-Sang Lee ◽  
Hyung-Jeong Yang ◽  
In-Seop Na ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Research Area ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Network Connections ◽  
Multi Level ◽  
Machine Interaction ◽  
Made In

Emotion recognition plays an indispensable role in human–machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

scholarly journals Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020 ◽  
Vol 7 ◽  
Author(s):  
Matteo Spezialetti ◽  
Giuseppe Placidi ◽  
Silvia Rossi
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Brain Activity ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Robot Interaction ◽  
Human Machine Interaction ◽  
Recent Advances ◽  
Critical Issues ◽  
Machine Interaction

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

scholarly journals Speech Emotion Recognition Using MLP Classifier

2021 ◽  
pp. 218-222
Author(s):  
Nagaraja N Poojary ◽  
Dr. Shivakumar G S ◽  
Akshath Kumar B.H
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Social Interaction ◽  
Emotion Recognition ◽  
Basic Medium ◽  
Speech Emotion Recognition ◽  
Speech Sample ◽  
Human Machine Interaction ◽  
Mlp Classifier ◽  
Machine Interaction

Language is human's most important communication and speech is basic medium of communication. Emotion plays a crucial role in social interaction. Recognizing the emotion in a speech is important as well as challenging because here we are dealing with human machine interaction. Emotion varies from person to person were same person have different emotions all together has different way express it. When a person express his emotion each will be having different energy, pitch and tone variation are grouped together considering upon different subject. Therefore the speech emotion recognition is a future goal of computer vision. The aim of our project is to develop the smart emotion recognition speech based on the convolutional neural network. Which uses different modules for emotion recognition and the classifier are used to differentiate emotion such as happy sad angry surprise. The machine will convert the human speech signals into waveform and process its routine at last it will display the emotion. The data is speech sample and the characteristics are extracted from the speech sample using librosa package. We are using RAVDESS dataset which are used as an experimental dataset. This study shows that for our dataset all classifiers achieve an accuracy of 68%.

scholarly journals A Review of Emotion Recognition Based on EEG using DEAP Dataset

2021 ◽  
pp. 298-303
Author(s):  
Rama Chaudhary ◽  
Ram Avtar Jaswal
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Physiological Signals ◽  
Emotional States ◽  
Modern Time ◽  
Human Machine Interaction ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Machine Interaction

In modern time, the human-machine interaction technology has been developed so much for recognizing human emotional states depending on physiological signals. The emotional states of human can be recognized by using facial expressions, but sometimes it doesn’t give accurate results. For example, if we detect the accuracy of facial expression of sad person, then it will not give fully satisfied result because sad expression also include frustration, irritation, anger, etc. therefore, it will not be possible to determine the particular expression. Therefore, emotion recognition using Electroencephalogram (EEG), Electrocardiogram (ECG) has gained so much attraction because these are based on brain and heart signals respectively. So, after analyzing all the factors, it is decided to recognize emotional states based on EEG using DEAP Dataset. So that, the better accuracy can be achieved.

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