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.
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.
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.
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.
This paper aims to open the discussion about historian’s emotions during the research process that has mostly been covered up. It does not pretend to be a thorough account of the topic but a modest essay that might encourage other researcher to reflect on their experiences. Firstly, we briefly describe the current situation in a few neighboring disciplines. Secondly, we explain how we understand emotions and use the terms emotion, feeling and sentiment. Thirdly, we discuss the reasons why most historians keep silent about their feelings. Fourthly, with two examples, we illustrate how historians have written about their emotions. Fifthly, we present a model of emotional phases of research by the Danish social psychologist Steinar Kvale and evaluate its relevance to historical research. Then we look at the causes and/or objects of feelings of students or beginning scholars in cultural history. Finally, we suggest some ways we historians could make our scholarly community emotionally a more supportive one. It might be good to remember that our discussion concerns primarily the Finnish academic world, and the situation in other countries might be slightly different.
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.
The author analyses problems of disease, dying, and death addressed in a play by Margaret Edson entitled Wit. Special attention is paid to the structure of meta-theatre and the function of wit in the play. The author investigates limitations of reason in the approach adopted by the doctors who take care of Vivian Bearing, and who subject her to an excruciating experiment in order to achieve a potential research success. She also discusses the protagonist’s attitude to literary works, dealing with her own disease, to other people and to God. This offers an opportunity to ruminate on the exact meaning of irretrievable loss involved in suffering. She also concentrates on the attitude of the nurse who – thanks to her emotional intelligence and empathy – accompanies Vivian on her way to death.
This study discusses the cross-cultural re-conceptualization of the slogan ‘I’m lovin’ it’, popularized in Poland by a global fast-food restaurant chain, which occurs in the inter-linguistic transfer between English and Polish. The analytical framework for the study is provided by Cultural Linguistics and the Re-conceptualization and Approximation Theory. The analysis is based on proposals submitted by 45 translators asked to come up with a Polish equivalent of the slogan. The results indicate that because the semantic networks for the meaning of love do not overlap between English and Polish perfectly, attempts at the cross-cultural transfer of the slogan can be approached only as more or less accurate approximations of the original meaning constructed according to culture-specific norms, expectations, and attitudes.
Animals kept outside their natural environment often suffer from boredom. They don’t hunt or have a chance to conduct their mating rituals, and their natural tendency for physical activity is limited by space. These deficiencies affect their psychological well-being. But when it comes to dogs, we can help them by exploiting their excellent sense of smell.
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.
Studies based on the most common diagnostic categories do not bring conclusive results concerning the overlapping and distinctive features of anxiety and depression, especially in the areas of attentional functioning, structure of affect, and cognitive emotion regulation. However, a new typology has been proposed which treats anxiety and depression as personality types (Fajkowska, 2013). These types – arousal and apprehension anxiety as well as valence and anhedonic depression – are constructed based on two criteria: specific structure and functions (reactive or regulative). The present paper critically examines the empirical evidence related to this approach. The data mostly confirmed the prediction that the similarities and differences in attentional and affective functioning among the anxiety and depression types would be related to their shared and specific structural and functional characteristics. The new typology turned out to be suitable for integrating the existing research findings by relating them to the structure and functions of anxiety and depression. As a result, it is useful in explaining some of the inconsistencies in literature, as it allows to identify the overlapping and distinctive features of the anxiety and depression types. It also helps to understand the mechanisms contributing to the development and maintenance of anxiety and depression, which might be useful in diagnosis and treatment. However, even though Fajkowska’s approach is an important contribution to the understanding of anxiety and depression, it is not exhaustive. Its limitations are discussed, along with proposed modifications of the theory, as well as further research directions.
The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.
Due to an increasing amount of music being made available in digital form in the Internet, an automatic organization of music is sought. The paper presents an approach to graphical representation of mood of songs based on Self-Organizing Maps. Parameters describing mood of music are proposed and calculated and then analyzed employing correlation with mood dimensions based on the Multidimensional Scaling. A map is created in which music excerpts with similar mood are organized next to each other on the two-dimensional display.