Analysis and automatic identification of spontaneous emotions in speech from human-human and human-machine communication

383 p.This research mainly focuses on improving our understanding of human-human and human-machineinteractions by analysing paricipants¿ emotional status. For this purpose, we have developed andenhanced Speech Emotion Recognition (SER) systems for both interactions in real-life scenarios,explicitly emphasising the Spanish language. In this framework, we have conducted an in-depth analysisof how humans express emotions using speech when communicating with other persons or machines inactual situations. Thus, we have analysed and studied the way in which emotional information isexpressed in a variety of true-to-life environments, which is a crucial aspect for the development of SERsystems. This study aimed to comprehensively understand the challenge we wanted to address:identifying emotional information on speech using machine learning technologies. Neural networks havebeen demonstrated to be adequate tools for identifying events in speech and language. Most of themaimed to make local comparisons between some specific aspects; thus, the experimental conditions weretailored to each particular analysis. The experiments across different articles (from P1 to P19) are hardlycomparable due to our continuous learning of dealing with the difficult task of identifying emotions inspeech. In order to make a fair comparison, additional unpublished results are presented in the Appendix.These experiments were carried out under identical and rigorous conditions. This general comparisonoffers an overview of the advantages and disadvantages of the different methodologies for the automaticrecognition of emotions in speech

Machine Learning Algorithm for the Scansion of Old Saxon Poetry

Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

Applying health psychology in an academic environment

Introduction. With an ageing population, the health and well-being of older adults is one of the most pressing issues facing the world today. Subjective well-being refers to the way people evaluate the objective conditions of their life and is widely thought to consist of both affective and cognitive appraisal components. It has been found to be associated with a wide range of outcomes, including health, functioning, mortality, income and coping. Understanding the determinants of subjective well-being and the underlying mechanisms of these relationships is vital in identifying potential targets for intervention. This is particularly relevant in older adults, who experience increasing functional decline as part of the ageing process. Adopting a life course approach enables the investigation of the bio-psycho-social factors influencing well-being throughout life. This study utilises a unique sample of individuals studied extensively in childhood and early adulthood and followed up in old age. It aims to investigate the structure and life course determinants of subjective well-being in older adults. Methods. The 6-Day Sample of the Scottish Mental Survey consists of 1208 individuals born on 6 days of 1936 and followed up from the age of 11 to 27. 174 members of this group were recruited into a follow-up study at age 77, completing a questionnaire and physical testing measures. Childhood measures included background demographic factors, personality, and educational and occupational ambitions and attainment. Old age measures included social mobility, personality, optimism, resilience, mood, sense of coherence, stress reactivity (cortisol) and three measures of subjective well-being (life satisfaction, mental well-being and mental health). Results. The three individual measures of well-being were found to load onto single traits with satisfactory to poor fit. A confirmatory factor analysis of all well-being items suggested a modest fit to a model incorporating two inter-related latent traits of affective and cognitive well-being. None of the early career or occupational goal attainment factors were associated with well-being, with the exception of goal change in women. Job instability was found to be associated with sense of coherence manageability in men and resilience and sense of coherence comprehensibility in women, although in opposite directions. There were no associations between measures of stress reactivity and well-being. Hierarchical regression analyses suggested that the strongest determinants of subjective well-being in this group are current anxiety, current depression, and sense of coherence, with significant contributions from the Big Five personality traits of extraversion, conscientiousness, and emotional stability. Conclusions. The results have implications for improving subjective well-being in older adults. Anxiety and depression are important targets for intervention in older adults as they are associated with increased mortality risk and cognitive decline. A strong sense of coherence is important in old age as it has been associated with a variety of positive health and well-being outcomes. The current study highlights the importance of these three factors and personality traits in determining well-being in old age, and illuminates some of the potential mechanisms for these relationships

Eighth International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2020)

International audienceProceedings of the 8th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2020)co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, August 29, 202