Search results for: speech emotion recognition

Authors: Manjare Chandraprabha A., S. D. Shirbahadurkar, Manjare Anil S., Paithne Ajay N.

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This paper proposes Intonation Modeling for Prosody generation in Neutral speech for Marathi (language spoken in Maharashtra, India) story telling applications. Nowadays audio story telling devices are very eminent for children. In this paper, we proposed tilt model for stressed words in Marathi for speech modification. Tilt model predicts modification in tone of neutral speech. GMM is used to identify stressed words for modification.

Keywords: tilt model, fundamental frequency, statistical parametric speech synthesis, GMM

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Authors: Gautam Sharda

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The concept of right speech is the third stage of the noble eightfold path as prescribed by the Buddha and followed by millions of practicing Buddhists. The Buddha lays a lot of importance on the notion of right speech (Samma Vacca). In the Angutara Nikaya, the Buddha mentioned what constitutes right speech, which is basically four kinds of abstentions; namely abstaining from false speech, abstaining from slanderous speech, abstaining from harsh or hateful speech and abstaining from idle chatter. The Buddha gives reasons in support of his view as to why abstaining from these four kinds of speeches is favourable not only for maintaining the peace and equanimity within an individual but also within a society. It is a known fact that when we say something harsh or slanderous to others, it eventually affects our individual peace of mind too. We also know about the many examples of hate speeches which have led to senseless cases of violence and which are well documented within our country and the world. Also, indulging in false speech is not a healthy sign for individuals within a group as this kind of a social group which is based on falsities and lies cannot really survive for long and will eventually lead to chaos. Buddha also told us to refrain from idle chatter or gossip as generally we have seen that idle chatter or gossip does more harm than any good to the individual and the society. Hence, if most of us actually inculcate this third stage (namely, right speech) of the noble eightfold path of the Buddha in our daily life, it would be highly beneficial both for the individual and for the harmony of the society.

Keywords: Buddhism, speech, individual, society

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Authors: Usha Barahmand, Ali Khazaee, Goudarz Sadeghi Hashjin

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Coping motives are considered to be indicators of problematic substance use. The present investigation examined a model with emotional abuse as an antecedent and emotional dysregulation as a mediator leading to substance use. The intent of this study was to examine the associations between various types of childhood maltreatment and motives for substance use. The sample consisted of 72 male opiate users recruited from those enrolled for Methadone Maintenance treatment. Participants responded to measures of childhood maltreatment, emotion dysregulation, and motives for opiate use. All data were analyzed using Pearson's correlation coefficients and bootstrap analysis of mediation. Results supported the hypothesis that the experience of emotional abuse in childhood is associated with problems in regulating emotions which in turn correlates with opiate use as a way to cope with negative affect, to enhance positive effect or to obtain social rewards. Bootstrap analysis confirmed the mediating role of emotion dysregulation. Findings support the potential utility of further research into emotion dysregulation and motives as antecedents of problematic opiate use.

Keywords: childhood abuse, emotion dysregulation, motives, substance use

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Authors: Tetyana Baydyk, Ernst Kussul, Sandra Bonilla Meza

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There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild) face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face.

Keywords: face recognition, labeled faces in the wild (LFW) database, random local descriptor (RLD), random features

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Authors: Jodie Stevenson, Lisa-Marie Emerson, Abigail Millings

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Mindfulness has been conceptualized as a dispositional trait, which is different across individuals. Previous research has independently identified both adult attachment orientations and emotion regulation abilities as correlates of dispositional mindfulness. Research has also presented a two-factor model of the relationship between these three constructs. The present study aimed to further develop this model and investigated theses relationships in a sample of 186 participants. Participants completed the Five Factor Mindfulness Questionnaire Short Form (FFMQ-SF), the Experiences in Close Relationships Scale for global attachment (ECR), the Emotion Regulation Questionnaire (ERC), and the Adult Disorganized Attachment scale (ADA). Exploratory factor analysis revealed a 3-factor solution accounting for 59% of the variance across scores on these measures. The first factor accounted for 32% of the variance and loaded highly on attachment and mindfulness subscales. The second factor accounted for 15% of the variance with strong loadings on emotion regulation subscales. The third factor accounted for 12% of the variance with strong loadings on disorganized attachment, and the mindfulness observes subscale. The results further confirm the relationship between attachment, mindfulness, and emotion regulation along with the unique addition of disorganized attachment. The extracted factors will then be used to predict well-being outcomes for an undergraduate student population.

Keywords: adult attachment, emotion regulation, mindfulness, well-being

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Chafik Barnoussi, Mourad Talbi, Adnane Cherif

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In this paper we propose a new speech compression system based on the application of the Bionic Wavelet Transform (BWT) combined with the psychoacoustic model. This compression system is a modified version of the compression system using a MDCT (Modified Discrete Cosine Transform) filter banks of 32 filters each and the psychoacoustic model. This modification consists in replacing the banks of the MDCT filter banks by the bionic wavelet coefficients which are obtained from the application of the BWT to the speech signal to be compressed. These two methods are evaluated and compared with each other by computing bits before and bits after compression. They are tested on different speech signals and the obtained simulation results show that the proposed technique outperforms the second technique and this in term of compressed file size. In term of SNR, PSNR and NRMSE, the outputs speech signals of the proposed compression system are with acceptable quality. In term of PESQ and speech signal intelligibility, the proposed speech compression technique permits to obtain reconstructed speech signals with good quality.

Keywords: speech compression, bionic wavelet transform, filterbanks, psychoacoustic model

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Authors: Yi-Ju Lee

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This study identified the hypothesized relationships among tourist emotion, creative experience, and behavioral intention of handmade ancient candy in Tainan, Taiwan. A face-to-face questionnaire survey was administered in Anping, Tainan. The result also revealed significant positive relationships between emotion, creative experience and behavioral intention in handmade activities. This paper provides additional suggestions for enhancing behavioral intention and guidance regarding creative tourism.

Keywords: creative tourism, sense of achievement, unique learning, interaction with instructors

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Authors: Yuri Kitahara

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Background: Cognitive reappraisal refers to an emotion regulation strategy in which one changes the interpretation of emotion-eliciting events. Numerous studies show that cognitive reappraisal is associated with mental health and better social functioning. However the examination of the predictive factors of adaptive emotion regulation remains as an issue. The present study examined the factors contributing to the habitual use of cognitive reappraisal, with a focus on emotional awareness and working memory. Methods: Data was collected from 30 junior high school students, using a Japanese version of the Emotion Regulation Questionnaire (ERQ), the Levels of Emotional Awareness Scale for Children (LEAS-C), and N-back task. Results: A positive correlation between emotional awareness and cognitive reappraisal was observed in the high-working-memory group (r = .54, p < .05), whereas no significant relationship was found in the low-working-memory group. In addition, the results of the analysis of variance (ANOVA) showed a significant interaction between emotional awareness and working memory capacity (F(1, 26) = 7.74, p < .05). Subsequent analysis of simple main effects confirmed that high working memory capacity significantly increases the use of cognitive reappraisal for high-emotional-awareness subjects, and significantly decreases the use of cognitive reappraisal for low-emotional-awareness subjects. Discussion: These results indicate that under the condition when one has an adequate ability for simultaneous processing of information, explicit understanding of emotion would contribute to adaptive cognitive emotion regulation. The findings are discussed along with neuroscientific claims.

Keywords: cognitive reappraisal, emotional awareness, emotion regulation, working memory

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Authors: Lizel Bertie, Kim Johnston

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Parental emotion regulation plays a central role in the socialisation of emotion, especially when teaching young children to cope with negative emotions. Despite evidence which shows non-supportive parental responses to children’s expression of negative emotions has implications for the social and emotional development of the child, few studies have investigated risk factors which impact parental emotion socialisation processes. The current study aimed to explore the extent to which parental stress contributes to both difficulties in parental emotion regulation and non-supportive parental responses to children’s expression of negative emotions. In addition, the study examined whether parental use of expressive suppression as an emotion regulation strategy facilitates the influence of parental stress on non-supportive responses by testing the relations in a mediation model. A sample of 140 Australian adults, who identified as parents with children aged 5 to 10 years, completed an online questionnaire. The measures explored recent symptoms of depression, anxiety, and stress, the use of expressive suppression as an emotion regulation strategy, and hypothetical parental responses to scenarios related to children’s expression of negative emotions. A mediated regression indicated that parents who reported higher levels of stress also reported higher levels of expressive suppression as an emotion regulation strategy and increased use of non-supportive responses in relation to young children’s expression of negative emotions. These findings suggest that parents who experience heightened symptoms of stress are more likely to both suppress their emotions in parent-child interaction and engage in non-supportive responses. Furthermore, higher use of expressive suppression strongly predicted the use of non-supportive responses, despite the presence of parental stress. Contrary to expectation, no indirect effect of stress on non-supportive responses was observed via expressive suppression. The findings from the study suggest that parental stress may become a more salient manifestation of psychological distress in a sub-clinical population of parents while contributing to impaired parental responses. As such, the study offers support for targeting overarching factors such as difficulties in parental emotion regulation and stress management, not only as an intervention for parental psychological distress, but also the detection and prevention of maladaptive parenting practices.

Keywords: emotion regulation, emotion socialisation, expressive suppression, non-supportive responses, parental stress

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Authors: Mirela-Gabriela Bratu

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The main objective of this study is to highlight the way in which emotions are constructed discursively in the French written press, more particularly in the titles of informative articles. To achieve this objective, we will begin the study with the theoretical part, which aims to capture the characteristics of journalistic discourse, to which we will add clues of emotions that we will identify in the titles of the articles. The approach is based on the empirical results from the analysis of the articles published on the earthquake that took place on August 24, 2016, in Italy, as described by two French national daily newspapers: Le Monde and Le Point. The corpus submitted to the analysis contains thirty-seven titles, published between August 24, 2016, and August 24, 2017. If the textual content of the speech offers information respecting the grammatical standards and following the presentation conventions, the choice of words can touch the reader, so the journalist must add other means than mastering of the language to create emotion. This study aims to highlight the strategies, such as rhetorical figures, the tenses, or factual data, used by journalists to create emotions for the readers. We also try, thanks to the study of the articles which were published for several days relating to the same event, to emphasize whether we can speak or not of the dissipation of emotion and the catastrophic side as the event fades away in time. The theoretical framework is offered by works on rhetorical strategies (Perelman, 1992; Amossi, 2000; Charaudeau, 2000) and on the study of emotions (Plantin, 1997, 1998, 2004; Tetu, 2004).

Keywords: disaster, earthquake, emotion, feeling

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Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

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Authors: Martha Berg, Megan Ramaiya, Andi Schmidt, Susanna Sharma, Brandon Kohrt

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Nepali adolescents report tension and negative emotion due to perceived expectations of both academic and social achievement. These societal goals, which are internalized through early-life socialization, drive the development of self-regulatory processes such as emotion regulation. Emotion dysregulation is linked with adverse psychological outcomes such as depression, self-harm, and suicide, which are public health concerns for organizations working with Nepali adolescents. This study examined the relation among socialization, internalized cultural goals, and emotion regulation to inform interventions for reducing depression and suicide in this population. Participants included 102 students in grades 7 through 9 in a post-earthquake school setting in rural Kathmandu valley. All participants completed a tablet-based battery of quantitative measures, comprising transculturally adapted assessments of emotion regulation, depression, and self-harm/suicide ideation and behavior. Qualitative measures included two focus groups and semi-structured interviews with 22 students and 3 parents. A notable proportion of the sample reported depression symptoms in the past 2 weeks (68%), lifetime self-harm ideation (28%), and lifetime suicide attempts (13%). Students who lived with their nuclear family reported lower levels of difficulty than those who lived with more distant relatives (z=2.16, p=.03), which suggests a link between family environment and adolescent emotion regulation, potentially mediated by socialization and internalization of cultural goals. These findings call for further research into the aspects of nuclear versus extended family environments that shape the development of emotion regulation.

Keywords: adolescent mental health, emotion regulation, Nepal, socialization

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Authors: Samba Raju, Chiluveru, Manoj Tripathy

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Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methods

Keywords: discrete wavelet transform, speech intelligibility, STOI, standard deviation

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Authors: Sulistyaningtyas

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Examining the role of Middle Eastern freedom activists’ speech based on gender perspective is considered noteworthy because the society in the Middle East is patriarchal. This research aims to examine the language use of the Middle Eastern freedom activists’ speeches through gender perspective. The data sources are from male and female Middle Eastern freedom activists’ speech videos. In analyzing the data, the theories employed are about Language Style from Gender Perspective and The Language for Speech. The result reveals that there are sets of spoken language differences between male and female speakers. In using the language for speech, both male and female speakers produce metaphor, euphemism, the ‘rule of three’, parallelism, and pronouns in random frequency of production, which cannot be separated by genders. Moreover, it cannot be concluded that one gender is more potential than the other to influence the audience in delivering speech. There are other factors, particularly non-verbal factors, existing to give impacts on how a speech can influence the audience.

Keywords: gender perspective, language use, Middle Eastern freedom activists, speech

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Authors: Gabriela Smeckova

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The entire world is becoming more and more diverse. The reasons why people migrate are different and unique for each family /individual. Professionals delivering services (including speech-language pathologists) must be prepared to work with clients coming from different cultural and/or linguistic backgrounds. Well-educated speech-language pathologists will consider many factors when delivering services. Some of them will be discussed during the presentation (language spoken, beliefs about health care and disabilities, reasons for immigration, etc.). The communication styles of the client can be different than the styles of the speech-language pathologist. The goal is to become culturally responsive in service delivery.

Keywords: culture, cultural competence, culturallly responsive practices, speech-language pathologist, cultural and linguistical variables, communication styles

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Authors: Edwin Foo, Woei Wen, Lui, Meijun Zhao, Shigeru Kuchii, Chin Sai Wong, Chung Sern Goh, Yi Hao He

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This presentation presents an elderly care and assistive social robot development work. We named this robot JOS and he is restricted to table top operation. JOS is designed to have a maximum volume of 3600 cm3 with its base restricted to 250 mm and his mission is to provide companion, assist and help the elderly. In order for JOS to accomplish his mission, he will be equipped with perception, reaction and cognition capability. His appearance will be not human like but more towards cute and approachable type. JOS will also be designed to be neutral gender. However, the robot will still have eyes, eyelid and a mouth. For his eyes and eyelids, they will be built entirely with Robotis Dynamixel AX18 motor. To realize this complex task, JOS will be also be equipped with micro-phone array, vision camera and Intel i5 NUC computer and a powered by a 12 V lithium battery that will be self-charging. His face is constructed using 1 motor each for the eyelid, 2 motors for the eyeballs, 3 motors for the neck mechanism and 1 motor for the lips movement. The vision senor will be house on JOS forehead and the microphone array will be somewhere below the mouth. For the vision system, Omron latest OKAO vision sensor is used. It is a compact and versatile sensor that is only 60mm by 40mm in size and operates with only 5V supply. In addition, OKAO vision sensor is capable of identifying the user and recognizing the expression of the user. With these functions, JOS is able to track and identify the user. If he cannot recognize the user, JOS will ask the user if he would want him to remember the user. If yes, JOS will store the user information together with the capture face image into a database. This will allow JOS to recognize the user the next time the user is with JOS. In addition, JOS is also able to interpret the mood of the user through the facial expression of the user. This will allow the robot to understand the user mood and behavior and react according. Machine learning will be later incorporated to learn the behavior of the user so as to understand the mood of the user and requirement better. For the speech system, Microsoft speech and grammar engine is used for the speech recognition. In order to use the speech engine, we need to build up a speech grammar database that captures the commonly used words by the elderly. This database is built from research journals and literature on elderly speech and also interviewing elderly what do they want to robot to assist them with. Using the result from the interview and research from journal, we are able to derive a set of common words the elderly frequently used to request for the help. It is from this set that we build up our grammar database. In situation where there is more than one person near JOS, he is able to identify the person who is talking to him through an in-house developed microphone array structure. In order to make the robot more interacting, we have also included the capability for the robot to express his emotion to the user through the facial expressions by changing the position and movement of the eyelids and mouth. All robot emotions will be in response to the user mood and request. Lastly, we are expecting to complete this phase of project and test it with elderly and also delirium patient by Feb 2015.

Keywords: social robot, vision, elderly care, machine learning

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Authors: Rajani S. Pujar, Pandurangarao N. Kulkarni

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Increased temporal masking affects the speech perception in persons with sensorineural hearing impairment especially under adverse listening conditions. This paper presents a cascaded scheme, which employs a noise reduction algorithm as well as temporal splitting of the speech signal. Earlier investigations have shown that by splitting the speech temporally and presenting alternate segments to the two ears help in reducing the effect of temporal masking. In this technique, the speech signal is processed by two fading functions, complementary to each other, and presented to left and right ears for binaural dichotic presentation. In the present study, half cosine signal is used as a fading function with crossover gain of 6 dB for the perceptual balance of loudness. Temporal splitting is combined with noise reduction algorithm to improve speech perception in the background noise. Two noise reduction schemes, namely spectral subtraction and Wiener filter are used. Listening tests were conducted on six normal-hearing subjects, with sensorineural loss simulated by adding broadband noise to the speech signal at different signal-to-noise ratios (∞, 3, 0, and -3 dB). Objective evaluation using PESQ was also carried out. The MOS score for VCV syllable /asha/ for SNR values of ∞, 3, 0, and -3 dB were 5, 4.46, 4.4 and 4.05 respectively, while the corresponding MOS scores for unprocessed speech were 5, 1.2, 0.9 and 0.65, indicating significant improvement in the perceived speech quality for the proposed scheme compared to the unprocessed speech.

Keywords: MOS, PESQ, spectral subtraction, temporal splitting, wiener filter

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Ajish K. Abraham

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Hearing aid is the most fundamental technology employed towards rehabilitation of persons with sensory neural hearing impairment. Hearing in noise is still a matter of major concern for many hearing aid users and thus continues to be a challenging issue for the hearing aid designers. Several techniques are being currently used to enhance the speech at the hearing aid output. Most of these techniques, when implemented, result in reduction of intelligibility of the speech signal. Thus the dissatisfaction of the hearing aid user towards comprehending the desired speech amidst noise is prevailing. Multichannel Wiener Filter is widely implemented in binaural hearing aid technology for noise reduction. In this study, Wiener filter based noise reduction approach is experimented for a single microphone based hearing aid set up. This method checks the status of the input speech signal in each frequency band and then selects the relevant noise reduction procedure. Results showed that the Wiener filter based algorithm is capable of enhancing speech even when the input acoustic signal has a very low Signal to Noise Ratio (SNR). Performance of the algorithm was compared with other similar algorithms on the basis of improvement in intelligibility and SNR of the output, at different SNR levels of the input speech. Wiener filter based algorithm provided significant improvement in SNR and intelligibility compared to other techniques.

Keywords: hearing aid output speech, noise reduction, SNR improvement, Wiener filter, speech enhancement

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Authors: Mehdi Destani, Asghar Heydari

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Objective: The present study aimed to investigate the association between pathological personality dimensions and emotion dysregulation through the mediating role of Cyberchondria behaviors among users of virtual networks. Materials and methods: A descriptive–correlational research method was used in this study, and the statistical population consisted of all people active on social network sites in 2020. The sample size was 300 people who were selected through Convenience Sampling. Data collection was carried out in a survey method using online questionnaires, including the "Difficulties in Emotion Regulation Scale" (DERS), Personality Inventory for DSM-5 Brief Form (PID-5-BF), and Cyberchondria Severity Scale Brief Form (CSS-12). Data analysis was conducted using Pearson's Correlation Coefficient and Structural Equation Modeling (SEM). Findings: Findings suggested that pathological personality dimensions and Cyberchondria behaviors have a positive and significant association with emotion dysregulation (p<0.001). The presented model had a good fit with the data. The variable “pathological personality dimensions” with an overall effect (p<0.001, β=0.658), a direct effect (p<0.001, β=0.528), and an indirect mediating effect through Cyberchondria Behaviors (p<.001), β=0.130), accounted for emotion dysregulation among virtual network users. Conclusion: The research findings showed a necessity to pay attention to the pathological personality dimensions as a determining variable and Cyberchondria behaviors as a mediator in the vulnerability of users of social network sites to emotion dysregulation.

Keywords: cyberchondria, emotion dysregulation, pathological personality dimensions, social networks

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Authors: Bing Gu

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Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system.

Keywords: DBN, face recognition, light field, Lytro

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Authors: Tanju Deveci

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It appears that the speech act of complaint has not received as much attention as other speech acts. However, the face-threatening nature of this speech act requires a special attention in multicultural contexts in particular. The teaching context in the UAE universities, where a big majority of teaching staff comes from other cultures, requires investigations into this speech act in order to improve communication between students and faculty. This session will outline the results of a study conducted with this purpose. The realization of complaints by Freshman English students in Communication courses at Petroleum Institute was investigated to identify communication patterns that seem to cause a strain. Data were collected using a role-play between a teacher and students, and a judgment scale completed by two of the instructors in the Communications Department. The initial findings reveal that the students had difficulty putting their case, produced the speech act of criticism along with a complaint and that they produced both requests and demands as candidate solutions. The judgement scales revealed that the students’ attitude was not appropriate most of the time and that the judges would behave differently from students. It is concluded that speech acts, in general, and complaint, in particular, need to be taught to learners explicitly to improve interpersonal communication in multicultural societies. Some teaching ideas are provided to help increase foreign language learners’ sociolinguistic competence.

Keywords: speech act, complaint, pragmatics, sociolinguistics, language teaching

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Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay

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Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.

Keywords: annotated facial expression dataset, gesture recognition, sequenced facial expression dataset, sign language recognition

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Authors: Telma Fontao de Castro, Kylee Miller, Maria Xavier Araújo, Isabel Brandao, Sandra Torres

Abstract:

Objective: There is a dearth of research investigating the long-term emotional functioning of individuals recovered from anorexia nervosa (AN). This 15-year longitudinal study aimed to examine whether difficulties in cognitive processing of emotions persisted after long-term AN recovery and its link to anxiety and depression. Method: Twenty-four females, who were tested longitudinally during their acute and recovered AN phases, and 24 healthy control (HC) women, were screened for anxiety, depression, alexithymia, and emotion regulation difficulties (ER; only assessed in recovery phase). Results: Anxiety, depression, and alexithymia levels decreased significantly with AN recovery. However, scores on anxiety and difficulty in identifying feelings (alexithymia factor) remained high when compared to the HC group. Scores on emotion regulation difficulties were also lower in HC group. The abovementioned differences between AN recovered group and HC group in difficulties in identifying and accepting feelings and lack of emotional clarity were no longer present when the effect of anxiety and depression was controlled. Conclusions: Findings suggest that emotional dysfunction tends to decrease in AN recovered phase. However, using an HC group as a reference, we conclude that several emotional difficulties are still increased after long-term AN recovery, in particular, limited access to emotion regulation strategies, and difficulty controlling impulses and engaging in goal-directed behavior, thus suggesting to be a trait vulnerability. In turn, competencies related to emotional clarity and acceptance of emotional responses seem to be state-dependent phenomena linked to anxiety and depression. In sum, managing emotions remains a challenge for individuals recovered from AN. Under this circumstance, maladaptive eating behavior can serve as an affect regulatory function, increasing the risk of relapse. Emotional education and stabilization of depressive and anxious symptomatology after recovery emerge as an important avenue to protect from long-term AN relapse.

Keywords: alexithymia, anorexia nervosa, emotion recognition, emotion regulation

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Authors: Yan Jia China

Abstract:

The ruins is a kind of historical intention, which is also the current real existence of developing city. Zen culture of ancient China has a profound esthetic emotion, similarly, the west establish the concept of aesthetics of relic along with the Romanism’s (such as Rousseau etc.) sentiment to historical ruins at the end of 18th century. Nowadays, with the decline of traditional industrial society as well as the rise of post-industrial age, contemporary society must face the ruins and garbage problem which is left by industrial society. Commencing from the perspective of emotion and memory, this paper analyzes the importance for emotional needs as well as their existing status of several projects, such as the Capital Steelworks in Beijing (industrial devastation), the Shibati old section in Chongqing (urban slums) and the Old Hurva Synagogue in Jerusalem (ruins of war). It emphasizes urban design which is started from emotion and the sustainable development of city memory through managing the urban ruins which is criticized by people with the perspective of ecology and art.

Keywords: cultural heritage, urban ruins, ecology, emotion, sustainable urban memory

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

Abstract:

This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: Tamara Matevosyan

Abstract:

The present paper discusses the main problems that students face while developing oral skills through authentic films. It states that special attention should be paid not only to the study of verbal speech but also to non-verbal communication. Authentic films serve as an important tool to understand both native speaker’s gestures and their culture of pausing while speaking. Various phonetic difficulties causing phonetic interference in actual speech are covered in the paper emphasizing the role of authentic films in overcoming them.

Keywords: compressive speech, filled pauses, unfilled pauses, pausing culture

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Authors: Yuchien Lin

Abstract:

This study attempted to investigate the changes in electroencephalography (EEG) and emotion regulation following eight-week inward-attention meditation training program. The subjects were 24 adults without meditation experiences divided into meditation and control groups. The quantitatively analyzed changes in psychophysiological parameters during inward-attention meditation, and evaluated the emotion scores assessed by the State-Trait Anxiety Inventory (STAI), the Positive and Negative Affect Schedule (PANAS), and the Emotion Regulation Scale (ERS). The results were found: (1) During meditation, significant EEG increased for theta-band activity in the frontal and the bilateral temporal areas, for alpha-band activity in the left and central frontal areas, and for gamma-band activity in the left frontal and the left temporal areas. (2) The meditation group had significantly higher positive affect in posttest than in pretest. (3) There was no significant difference in the changes of EEG spectral characteristics and emotion scores in posttest and pretest for the control group. In the present study, a unique meditative concentration task with a constant level of moderate mental effort focusing on the center of brain was used, so as to enhance frontal midline theta, alpha, and gamma-band activity. These results suggest that this mental training allows individual reach a specific mental state of relaxed but focused awareness. The gamma-band activity, in particular, enhanced over left frontoparietal area may suggest that inward-attention meditation training involves temporal integrative mechanisms and may induce short-term and long-term emotion regulation abilities.

Keywords: meditation, EEG, emotion regulation, gamma activity

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Authors: Shao-Di Feng, Von-Wun Soo

Abstract:

To generate symbolic music with specific emotions is a challenging task due to symbolic music datasets that have emotion labels are scarce and incomplete. This research aims to generate more refined emotions based on the training datasets that are only labeled with four quadrants in Russel’s 2D emotion model. We focus on the theory of Music Fadernet and map arousal and valence to the low-level attributes, and build a symbolic music generation model by combining transformer and GM-VAE. We adopt an in-attention mechanism for the model and improve it by allowing modulation by conditional information. And we show the music generation model could control the generation of music according to the emotions specified by users in terms of high-level linguistic expression and by manipulating their corresponding low-level musical attributes. Finally, we evaluate the model performance using a pre-trained emotion classifier against a pop piano midi dataset called EMOPIA, and by subjective listening evaluation, we demonstrate that the model could generate music with more refined emotions correctly.

Keywords: music generation, music emotion controlling, deep learning, semi-supervised learning

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