Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs). The emotion class model and the dimension space model are both adopted for the evaluation of the emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified. Considering the dimension space model, the arousal dimension and the valence dimension are classified into positive regions or negative regions. The experimental results show that the speech enhancement algorithms constantly improve the performance of our emotion recognition system under various SNRs, and the positive emotions are more likely to be miss-classified as negative emotions under white noise environment.

Dr. Magdelana Markowska from the University of Warsaw’s Faculty of Biology explains where emotions come from and why negative emotions are not the only ones that are problematic for the body.

Speech emotion recognition is an important part of human-machine interaction studies. The acoustic analysis method is used for emotion recognition through speech. An emotion does not cause changes on all acoustic parameters. Rather, the acoustic parameters affected by emotion vary depending on the emotion type. In this context, the emotion-based variability of acoustic parameters is still a current field of study. The purpose of this study is to investigate the acoustic parameters that fear affects and the extent of their influence. For this purpose, various acoustic parameters were obtained from speech records containing fear and neutral emotions. The change according to the emotional states of these parameters was analyzed using statistical methods, and the parameters and the degree of influence that the fear emotion affected were determined. According to the results obtained, the majority of acoustic parameters that fear affects vary according to the used data. However, it has been demonstrated that formant frequencies, mel-frequency cepstral coefficients, and jitter parameters can define the fear emotion independent of the data used.

Emotional intelligence (EI) is conceptualized as a personality trait or an ability. Most of conducted studies on EI-coping association referred to trait emotional intelligence. Therefore, the role of ability emotional intelligence is less clear and need to be further studied. The present study examined the relationship between two EI abilities (emotion recognizing and emotion understanding) and stress coping strategies in adolescent men and women. The data were collected from 1033 Polish high school students (520 men and 512 women) aged 18-20 years (Mage = 18.46 years). Coping strategies were assessed using the COPE inventory and emotional abilities were measured using the Emotional Intelligence Scale – Faces (SIE-T) and the Emotion Understanding Test (TRE). The results supported the existence of an association between EI abilities and coping strategies. The analyses of the interaction effects revealed the moderating role of gender on some of the relationships between EI abilities and coping strategies.

The cognitive aspects like perception, problem-solving, thinking, task performance, etc., are immensely influenced by emotions making it necessary to study emotions. The best state of emotion is the positive unexcited state, also known as the HighValence LowArousal (HVLA) state of the emotion. The psychologists endeavour to bring the subjects from a negatively excited state of emotion (Low Valence High Arousal state) to a positive unexcited state of emotion (High Valence Low Arousal state). In the first part of this study, a four-class subject independent emotion classifier was developed with an SVM polynomial classifier using average Event Related Potential (ERP) and differential average ERP attributes. The visually evoked Electroencephalogram (EEG) signals were acquired from 24 subjects. The four-class classification accuracy was 83% using average ERP attributes and 77% using differential average ERP attributes. In the second part of the study, the meditative intervention was applied to 20 subjects who declared themselves negatively excited (in Low Valence High Arousal state of emotion). The EEG signals were acquired before and after the meditative intervention. The four-class subject independent emotion classifier developed in Study 1 correctly classified these 20 subjects to be in a negatively excited state of emotion. After the intervention, 16 subjects self-assessed themselves to be in a positive unexcited (HVLA) state of emotion (which shows the intervention accuracy of 80%). Testing a four-class subject independent emotion classifier on the EEG data acquired after the meditative intervention validated 13 of 16 subjects in a positive unexcited state, yielding an accuracy of 81.3%.

This paper concerns measurement procedures on an emotion monitoring stand designed for tracking human emotions in the Human-Computer Interaction with physiological characteristics. The paper addresses the key problem of physiological measurements being disturbed by a motion typical for human-computer interaction such as keyboard typing or mouse movements. An original experiment is described, that aimed at practical evaluation of measurement procedures performed at the emotion monitoring stand constructed at GUT. Different locations of sensors were considered and evaluated for suitability and measurement precision in the Human- Computer Interaction monitoring. Alternative locations (ear lobes and forearms) for skin conductance, blood volume pulse and temperature sensors were proposed and verified. Alternative locations proved correlation with traditional locations as well as lower sensitiveness to movements like typing or mouse moving, therefore they can make a better solution for monitoring the Human-Computer Interaction.

Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A similar effect is seen with male speakers: the first model yields 36%, the second 28% a verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.

Empathy is one of the traits that make us human. In exploring the origins of empathy disorders, however, we can learn a lot by studying animals.

Why is it that people can end up interpreting what is being said to them in such different ways? A lot depends on whether they happen to be in a good or bad mood.

This paper addresses issues of feminism, masculinity, and the emotional culture of middle-class men who self-declare as feminists. The author discusses feminist theories on masculinity and its relations with femininity, critical theories of masculinity, and the role of emotional culture in the expression of masculinity. Feminists have proposed a dimorphic definition of feminism as a political movement and personal attitude critical of masculine domination. The critique of patriarchal, hegemonic masculinity has led feminists either to identify with “positive” masculinity or to reject masculinity for a post-gender narrative or material-discursive fact of “being a man,” which suggests an inadequacy of the sex/gender distinction in the description of gender identity. The identification with feminism allows men to avoid the crisis of traditional masculinity and the perspective of gendered emotions, as well as to gain insight into gendered determinants of emotional expression.

This study aimed to compare measures of religiosity and spirituality in the experience of positive and negative emotions. For this purpose, a measure of non-spiritual religiosity (Religious Sense Scale) was developed. Method: The study has been conducted on a sample of 279 participants aged between 19 and 69 (M=24.42, SD=9.463) who completed a questionnaire that included the Religious Sense Scale, the Portuguese version of the Spiritual Well-being Questionnaire and the abridged Portuguese version of the Positive and Negative Affect Schedule. Findings: The was found to have excellent metrical properties for the measurement of religiosity or “religious sense”. Religious individuals differ from spiritual ones in the experience of emotions: spirituality tends to a greater experience of positive affect and religiosity to negative affect.

An analysis of low-level feature space for emotion recognition from the speech is presented. The main goal was to determine how the statistical properties computed from contours of low-level features influence the emotion recognition from speech signals. We have conducted several experiments to reduce and tune our initial feature set and to configure the classification stage. In the process of analysis of the audio feature space, we have employed the univariate feature selection using the chi-squared test. Then, in the first stage of classification, a default set of parameters was selected for every classifier. For the classifier that obtained the best results with the default settings, the hyperparameter tuning using cross-validation was exploited. In the result, we compared the classification results for two different languages to find out the difference between emotional states expressed in spoken sentences. The results show that from an initial feature set containing 3198 attributes we have obtained the dimensionality reduction about 80% using feature selection algorithm. The most dominant attributes selected at this stage based on the mel and bark frequency scales filterbanks with its variability described mainly by variance, median absolute deviation and standard and average deviations. Finally, the classification accuracy using tuned SVM classifier was equal to 72.5% and 88.27% for emotional spoken sentences in Polish and German languages, respectively.

Prof. Roman Cieślak from the SWPS University of Social Sciences and Humanities talks about the emotional challenges of pursuing a career in science.

The article aims to investigate the problem of desemanticizing of phrasemes containing names of body parts, and at the same time referring to the emotional sphere. Within the three main research areas (face, heart and body as a whole), and based on three types of semes (spatial, physical and functional), the analysis allows to determine the participation of individual sems in the process of motivating the indicated phraseological relationships.

One of the major subjects that construct the emotional right-wing script is the history of the postwar Polish independence Underground and the related present-day politics and historical policy. The analysis of the right-wing press enables the distinction of four temporal categories to which specific toposes can be assigned as well as the moulded emotional elements: 1) the period of struggle, 2) the period of imprisonment and possible death, 3) the period of the Third Republic [of Poland], and 4) the period from the victory of the Law and Justice party (PiS) in the parliamentary elections until the present.

The aim of the study was to investigate the relationships between emotional intelligence (EI) and temperament. It was assumed that the two main components of EI – experiential and strategic – have different temperament correlates. One hundred and four Polish university students aged 19 to 26 completed self-descriptive questionnaires of temperament and emotional intelligence. The results confirmed that the relationship with temperament depends on the examined component of EI. Acceptance of emotions (which is a subcomponent of experiential EI) only correlated with two temperamental traits – activity and briskness. Many more dependencies were found in relation to strategic EI. Endurance, strength of inhibition, sensory sensitivity and perseveration turned out to be significant predictors of emotional control, which jointly explained 44% of the variance in results, while perseveration and sensory sensitivity explained 28% of the variance in results on the understanding emotions scale. Based on the results obtained, it can be assumed that the configuration of temperament traits that determines a high capacity for processing stimulation is most conductive to strategic EI. Other propitious traits include those that determine the speed of neural processes, flexibility and ease of adaptation to changing conditions as well as a low sensitivity threshold to sensory stimulus.

Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

The study of emotion regulation constitutes a major area of research for having a complete picture of human emotional experience, and several lines of evidence claim that poor emotion regulation skills are particularly deleterious in different aspects of life. Previous tDCS studies have suggested the beneficial role of DLPFC stimulation to improve emotion processing and regulation. The present study was therefore conducted to confirm and extend the effects of DLPFC stimulation on emotion regulation by including both positive and negative emotional material. In this between subjects study, participants were randomly assigned to receive active or sham stimulation over the left DLPFC. Participants viewed negative, positive, and neutral pictures while attempting to decrease, increase, or not modulate their emotional reactions. Subjective reactions were assessed via on-line ratings. The main results show that anodal tDCS stimulation over the left DLPFC slightly improves the ability to increase emotion perception for positive emotions. More interestingly, the results demonstrate that tDCS enhances the regulation of both positive and negative emotions when the baseline is considered. This study provides additional data on the use of tDCS as a tool to increase emotion regulation not only for negative affective material, but also for positive ones.

Covid-19 pandemic is severely impacting worldwide. A line of research warned that facial occlusion may impair facial emotion recognition, whilst prior research highlighted the role of Trait Emotional Intelligence in the recognition of non-verbal social stimuli. The sample consisted of 102 emerging adults, aged 18-24 (M = 20.76; SD = 2.10; 84% females, 16% males) and were asked to recognize four different emotions (happiness, fear, anger, and sadness) in fully visible faces and in faces wearing a mask and to complete a questionnaire assessing Trait Emotional Intelligence. Results highlighted that individuals displayed lower accuracy in detecting happiness and fear in covered faces, while also being more inaccurate in reporting correct answers. The results show that subjects provide more correct answers when the photos show people without a mask than when they are wearing it. In addition, participants give more wrong answers when there are subjects wearing masks in the photos than when they are not wearing it. In addition, participants provide more correct answers regarding happiness and sadness when in the photos the subjects are not wearing the mask, compared to when they are wearing it. Implications are discussed.

The domain of motion events is widely used to metaphorically describe abstract concepts, particularly emotional states. Why motion events are effective for describing abstract concepts is the question that this article intends to answer. In the literature of the field, several reasons have been suggested to be behind the suitability of motion events for describing these concepts, such as high concreteness of motion events, their high imageability, and the ability of comprehender to simultaneously imagine components of motion events. This article suggests that motion events are particularly effective for metaphorical description of those domains which have the feature of dynamic change over a period of time. This is particularly the case with emotional states. Since changes in emotions take place throughout a period of time, they could best be described by motion events which have the same feature. In other words, the continuous change in emotions is understood in terms of continuous change in the location of a moving object in the 3D space. Based on the arguments of embodied theories of cognition, it would be no surprise to see the involvement of similar areas of the brain in understanding emotions and motions.

Cheerleading is a new sport, practiced in 110 nations; since 2016 enjoys provisional Olympic status. Its leaders claim that it is a “happy” sport, but research on its psychological effects is lacking. In this field-study we examined core-affect, positive-affect, and negative-affect in 65 cheerleaders before, during, after, and one-hour after a cheerleading training. Core-affect was more positive during and immediately after training, but it tapered off one hour following the training when feeling states were still more positive than at baseline. Negative-affect declined linearly from baseline to one-hour following training when it became significantly lower than its previous values. Positive-affect showed quadratic dynamics, in parallel with arousal, being higher during and immediately after training than during baseline, or one-hour after training. These results demonstrate for the first time that cheerleading is a “happy” sport, which apart from the skill-development also yields positive psychological emotions both during and after training.