Semi-Supervised Speech Emotion Recognition with Ladder Networks

Speech emotion recognition (SER) systems find applications in various fields such as healthcare, education, and security and defense. A major drawback of these systems is their lack of generalization across different conditions. This problem can be solved by training models on large amounts of labeled data from the target domain, which is expensive and time-consuming. Another approach is to increase the generalization of the models. An effective way to achieve this goal is by regularizing the models through multitask learning (MTL), where auxiliary tasks are learned along with the primary task. These methods often require the use of labeled data which is computationally expensive to collect for emotion recognition (gender, speaker identity, age or other emotional descriptors). This study proposes the use of ladder networks for emotion recognition, which utilizes an unsupervised auxiliary task. The primary task is a regression problem to predict emotional attributes. The auxiliary task is the reconstruction of intermediate feature representations using a denoising autoencoder. This auxiliary task does not require labels so it is possible to train the framework in a semi-supervised fashion with abundant unlabeled data from the target domain. This study shows that the proposed approach creates a powerful framework for SER, achieving superior performance than fully supervised single-task learning (STL) and MTL baselines. The approach is implemented with several acoustic features, showing that ladder networks generalize significantly better in cross-corpus settings. Compared to the STL baselines, the proposed approach achieves relative gains in concordance correlation coefficient (CCC) between 3.0% and 3.5% for within corpus evaluations, and between 16.1% and 74.1% for cross corpus evaluations, highlighting the power of the architecture

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Emotional intelligence, reflective abilities and wellbeing in social workers

Research reportIn order to inform the curriculum and the development of supportive structures to support the work-related wellbeing of trainee social workers, this research project had several aims. It examined the key motivators to enter social work, together with the sources of social support and the coping strategies that students draw on to help them manage the demands of study and placement experiences Several emotional and social competencies (i.e. emotional intelligence, reflective ability, empathy and social competence) are also investigated as potential predictors of resilience. Also examined was whether resilience predicted psychological distress, and the role played by resilience in the relationship between emotional intelligence and distress was assessed

The role of trait emotional intelligence and social and emotional skills in students’ emotional and behavioural strengths and difficulties : a study of Greek adolescents’ perceptions

The emergence of the Trait Emotional Intelligence construct shifted the interest in personality research to the investigation of the effect of global personality characteristics on behaviour. A second body of research in applied settings, the Social and Emotional Learning movement, emphasized the cultivation of emotional and social skills for positive relationships in a school environment. In this paper we investigate the role of both personality traits and social and emotional skills, in the occurrence of emotional and behavioural strengths and difficulties, according to adolescent students’ self-perceptions. Five hundred and fifty-nine students from state secondary schools in Greece, aged 12-14 years old, completed The Trait Emotional Intelligence Questionnaire-Adolescent Short Form, The Matson Evaluation of Social Skills with Youngsters, and The Strengths and Difficulties Questionnaire. It was found that students with higher Trait Emotional Intelligence and stronger social and emotional skills were less likely to present emotional, conduct, hyperactivity and peer difficulties and more likely to present prosocial behaviour. Gender was a significant factor for emotional difficulties and grade for peer difficulties. The paper describes the underlying mechanisms of students’ emotional and behavioural strengths and difficulties, and provides practical implications for educators to improve the quality of students’ lives in schools.peer-reviewe