scholarly journals Deep time-delay Markov network for prediction and modeling the stress and emotions state transition

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Barlian Henryranu Prasetio ◽  
Hiroki Tamura ◽  
Koichi Tanno
Keyword(s):  
Time Delay ◽  
State Transition ◽  
Emotional State ◽  
Emotional States ◽  
Deep Time ◽  
Training Time ◽  
Markov Network ◽  
Prediction Result ◽  
Novel Method ◽  
Ablation Experiment

Abstract To recognize stress and emotion, most of the existing methods only observe and analyze speech patterns from present-time features. However, an emotion (especially for stress) can change because it was triggered by an event while speaking. To address this issue, we propose a novel method for predicting stress and emotions by analyzing prior emotional states. We named this method the deep time-delay Markov network (DTMN). Structurally, the proposed DTMN contains a hidden Markov model (HMM) and a time-delay neural network (TDNN). We evaluated the effectiveness of the proposed DTMN by comparing it with several state transition methods in predicting an emotional state from time-series (sequences) speech data of the SUSAS dataset. The experimental results show that the proposed DTMN can accurately predict present emotional states by outperforming the baseline systems in terms of the prediction error rate (PER). We then modeled the emotional state transition using a finite Markov chain based on the prediction result. We also conducted an ablation experiment to observe the effect of different HMM values and TDNN parameters on the prediction result and the computational training time of the proposed DTMN.

scholarly journals Emotion Recognition from Skeletal Movements

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 646 ◽  
Author(s):  
Tomasz Sapiński ◽  
Dorota Kamińska ◽  
Adam Pelikant ◽  
Gholamreza Anbarjafari
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Body Movement ◽  
Experimental Results ◽  
Microsoft Kinect ◽  
Emotional States ◽  
Important Trend ◽  
Novel Method ◽  
Multimodal Emotion Recognition ◽  
Body Joints

Automatic emotion recognition has become an important trend in many artificial intelligence (AI) based applications and has been widely explored in recent years. Most research in the area of automated emotion recognition is based on facial expressions or speech signals. Although the influence of the emotional state on body movements is undeniable, this source of expression is still underestimated in automatic analysis. In this paper, we propose a novel method to recognise seven basic emotional states—namely, happy, sad, surprise, fear, anger, disgust and neutral—utilising body movement. We analyse motion capture data under seven basic emotional states recorded by professional actor/actresses using Microsoft Kinect v2 sensor. We propose a new representation of affective movements, based on sequences of body joints. The proposed algorithm creates a sequential model of affective movement based on low level features inferred from the spacial location and the orientation of joints within the tracked skeleton. In the experimental results, different deep neural networks were employed and compared to recognise the emotional state of the acquired motion sequences. The experimental results conducted in this work show the feasibility of automatic emotion recognition from sequences of body gestures, which can serve as an additional source of information in multimodal emotion recognition.

Effect of Emotions in a Lexical Decision Task

2017 ◽  
Vol 76 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Hélène Maire ◽  
Renaud Brochard ◽  
Jean-Luc Kop ◽  
Vivien Dioux ◽  
Daniel Zagar
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional State ◽  
Control Group ◽  
Decision Task ◽  
Attentional Orienting ◽  
Emotional States ◽  
Sequence Effect ◽  
Emotion Induction ◽  
Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

scholarly journals Dogs can infer implicit information from human emotional expressions

2021 ◽  
Author(s):  
Natalia Albuquerque ◽  
Daniel S. Mills ◽  
Kun Guo ◽  
Anna Wilkinson ◽  
Briseida Resende
Keyword(s):  
Emotional State ◽  
Food Resource ◽  
Emotional Expressions ◽  
Emotional States ◽  
Emotional Information ◽  
Implicit Information ◽  
Emotional Display ◽  
Affective Information ◽  
Social Decision Making ◽  
Current Behaviour

AbstractThe ability to infer emotional states and their wider consequences requires the establishment of relationships between the emotional display and subsequent actions. These abilities, together with the use of emotional information from others in social decision making, are cognitively demanding and require inferential skills that extend beyond the immediate perception of the current behaviour of another individual. They may include predictions of the significance of the emotional states being expressed. These abilities were previously believed to be exclusive to primates. In this study, we presented adult domestic dogs with a social interaction between two unfamiliar people, which could be positive, negative or neutral. After passively witnessing the actors engaging silently with each other and with the environment, dogs were given the opportunity to approach a food resource that varied in accessibility. We found that the available emotional information was more relevant than the motivation of the actors (i.e. giving something or receiving something) in predicting the dogs’ responses. Thus, dogs were able to access implicit information from the actors’ emotional states and appropriately use the affective information to make context-dependent decisions. The findings demonstrate that a non-human animal can actively acquire information from emotional expressions, infer some form of emotional state and use this functionally to make decisions.

An experimental study on the effect of emotion lines in comics

Semiotica ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amitash Ojha ◽  
Charles Forceville ◽  
Bipin Indurkhya
Keyword(s):  
Experimental Study ◽  
Emotional State ◽  
Negative Emotions ◽  
Emotional States ◽  
One To One

Abstract Both mainstream and art comics often use various flourishes surrounding characters’ heads. These so-called “pictorial runes” (also called “emanata”) help convey the emotional states of the characters. In this paper, using (manipulated) panels from Western and Indian comic albums as well as neutral emoticons and basic shapes in different colors, we focus on the following two issues: (a) whether runes increase the awareness in comics readers about the emotional state of the character; and (b) whether a correspondence can be found between the types of runes (twirls, spirals, droplets, and spikes) and specific emotions. Our results show that runes help communicate emotion. Although no one-to-one correspondence was found between the tested runes and specific emotions, it was found that droplets and spikes indicate generic emotions, spirals indicate negative emotions, and twirls indicate confusion and dizziness.

A Novel Method for Gender and Age Detection Based on EEG Brain Signals

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Haitham Issa ◽  
Sali Issa ◽  
Wahab Shah
Keyword(s):  
Cross Validation ◽  
Image Feature ◽  
Emotional States ◽  
Time Frequency ◽  
Brain Signals ◽  
Average Accuracy ◽  
Gender And Age ◽  
Novel Method ◽  
Fold Cross Validation ◽  
Validation Strategy

This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

scholarly journals Will You Come Back to Contribute? Investigating the Inactivity of OSS Core Developers in GitHub

2021 ◽  
Author(s):  
Fabio Calefato ◽  
Marco Aurelio Gerosa ◽  
Giuseppe Iaffaldano ◽  
Filippo Lanubile ◽  
Igor Fabio Steinmacher
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
State Transition ◽  
Active State ◽  
State Model ◽  
Novel Method ◽  
To Come ◽  
One Year ◽  
The Individual ◽  
To Receive

Abstract Several Open-Source Software (OSS) projects depend on the continuity of their development communities to remain sustainable. Understanding how developers become inactive or why they take breaks can help communities prevent abandonment and incentivize developers to come back. In this paper, we propose a novel method to identify developers’ inactive periods by analyzing the individual rhythm of contributions to the projects. Using this method, we quantitatively analyze the inactivity of core developers in 18 OSS organizations hosted on GitHub. We also survey core developers to receive their feedback about the identified breaks and transitions. Our results show that our method was effective for identifying developers’ breaks. About 94% of the surveyed core developers agreed with our state model of inactivity; 71% and 79% of them acknowledged their breaks and state transition, respectively. We also show that all core developers take breaks (at least once) and about a half of them (~ 45%) have completely disengaged from a project for at least one year. We also analyzed the probability of transitions to/from inactivity and found that developers who pause their activity have a ~ 35 to ~ 55% chance to return to an active state; yet, if the break lasts for a year or longer, then the probability of resuming activities drops to ~ 21–26%, with a ~ 54% chance of complete disengagement. These results may support the creation of policies and mechanisms to make OSS community managers aware of breaks and potential project abandonment.

scholarly journals The role of emotional service in the restaurant business

2022 ◽  
pp. 164-167
Author(s):  
N. A. Ofitserova
Keyword(s):  
Emotional Intelligence ◽  
Customer Satisfaction ◽  
Emotional Labor ◽  
Work Performance ◽  
Positive Emotions ◽  
Emotional State ◽  
Communicative Competence ◽  
Point Of View ◽  
Emotional States

The article considers the restaurant business from the point of view of not only the entrepreneurial aspect, but also the service aspect, which is fundamental. The reasons why people visit restaurants have been revealed. In addition to physical need, restaurants are an element of cognition and a way of experiencing positive emotions. The importance of the restaurant business in shaping people’s positive emotional state has been formulated. Two forms of emotional labor of an employee and the influence of emotional states on work performance have been highlighted. The role of emotional intelligence and communicative competence in customer satisfaction with a restaurant visit has been determined. The importance of developing emotional intelligence has been concluded. Recommendations for its development has been formulated. 

scholarly journals Speech-based human emotion recognition

2021 ◽  
Author(s):  
Talieh Seyed Tabtabae
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Speaker Identification ◽  
Recognition System ◽  
Research Area ◽  
Speech Signals ◽  
Spectral Features ◽  
Emotional States ◽  
Human Communication ◽  
Set Up

Automatic Emotion Recognition (AER) is an emerging research area in the Human-Computer Interaction (HCI) field. As Computers are becoming more and more popular every day, the study of interaction between humans (users) and computers is catching more attention. In order to have a more natural and friendly interface between humans and computers, it would be beneficial to give computers the ability to recognize situations the same way a human does. Equipped with an emotion recognition system, computers will be able to recognize their users' emotional state and show the appropriate reaction to that. In today's HCI systems, machines can recognize the speaker and also content of the speech, using speech recognition and speaker identification techniques. If machines are equipped with emotion recognition techniques, they can also know "how it is said" to react more appropriately, and make the interaction more natural. One of the most important human communication channels is the auditory channel which carries speech and vocal intonation. In fact people can perceive each other's emotional state by the way they talk. Therefore in this work the speech signals are analyzed in order to set up an automatic system which recognizes the human emotional state. Six discrete emotional states have been considered and categorized in this research: anger, happiness, fear, surprise, sadness, and disgust. A set of novel spectral features are proposed in this contribution. Two approaches are applied and the results are compared. In the first approach, all the acoustic features are extracted from consequent frames along the speech signals. The statistical values of features are considered to constitute the features vectors. Suport Vector Machine (SVM), which is a relatively new approach in the field of machine learning is used to classify the emotional states. In the second approach, spectral features are extracted from non-overlapping logarithmically-spaced frequency sub-bands. In order to make use of all the extracted information, sequence discriminant SVMs are adopted. The empirical results show that the employed techniques are very promising.

Sign in / Sign up

Export Citation Format

Share Document