Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Children with special health care needs : social and learning competence in the early years

The number of children with special health care needs surviving infancy and attending school has been increasing. Due to their health status, these children may be at risk of low social-emotional and learning competencies (e.g., Lightfoot, Mukherjee, & Sloper, 2000; Zehnder, Landolt, Prchal, & Vollrath, 2006). Early social problems have been linked to low levels of academic achievement (Ladd, 2005), inappropriate behaviours at school (Shiu, 2001) and strained teacher-child relationships (Blumberg, Carle, O‘Connor, Moore, & Lippmann, 2008). Early learning difficulties have been associated with mental health problems (Maughan, Rowe, Loeber, & Stouthamer-Loeber, 2003), increased behaviour issues (Arnold, 1997), delinquency (Loeber & Dishion, 1983) and later academic failure (Epstein, 2008). Considering the importance of these areas, the limited research on special health care needs in social-emotional and learning domains is a factor driving this research. The purpose of the current research is to investigate social-emotional and learning competence in the early years for Australian children who have special health care needs. The data which informed this thesis was from Growing up in Australia: The Longitudinal Study of Australian Children. This is a national, longitudinal study being conducted by the Commonwealth Department of Families, Housing, Community Services and Indigenous Affairs. The study has a national representative sample, with data collection occurring biennially, in 2004 (Wave 1), 2006 (Wave 2) and 2008 (Wave 3). Growing up in Australia uses a cross-sequential research design involving two cohorts, an Infant Cohort (0-1 at recruitment) and a Kindergarten Cohort (4-5 at recruitment). This study uses the Kindergarten Cohort, for which there were 4,983 children at recruitment. Three studies were conducted to address the objectives of this thesis. Study 1 used Wave 1 data to identify and describe Australian children with special health care needs. Children who identified as having special health care needs through the special health care needs screener were selected. From this, descriptive analyses were run. The results indicate that boys, children with low birth weight and children from families with low levels of maternal education are likely to be in the population of children with special health care needs. Further, these children are likely to be using prescription medications, have poor general health and are likely to have specific condition diagnoses. Study 2 used Wave 1 data to examine differences between children with special health care needs and their peers in social-emotional competence and learning competence prior to school. Children identified by the special health care needs screener were chosen for the case group (n = 650). A matched case control group of peers (n = 650), matched on sex, cultural and linguistic diversity, family socioeconomic position and age, were the comparison group. Social-emotional competence was measured through Social/Emotional Domain scores taken from the Growing up in Australia Outcome Index, with learning competence measured through Learning Domain scores. Results suggest statistically significant differences in scores between the two groups. Children with special health care needs have lower levels of social-emotional and learning competence prior to school compared to their peers. Study 3 used Wave 1 and Wave 2 data to examine the relationship between special health care needs at Wave 1 and social-emotional competence and learning competence at Wave 2, as children started school. The sample for this study consisted of children in the Kindergarten Cohort who had teacher data at Wave 2. Results from multiple regression models indicate that special health care needs prior to school (Wave 1) significantly predicts social-emotional competence and learning competence in the early years of school (Wave 2). These results indicate that having special health care needs prior to school is a risk factor for the social-emotional and learning domains in the early years of school. The results from these studies give valuable insight into Australian children with special health care needs and their social-emotional and learning competence in the early years. The Australia population of children with special health care needs were primarily male children, from families with low maternal education, were likely to be of poor health and taking prescription medications. It was found that children with special health care needs were likely to have lower social-emotional competence and learning competence prior to school compared to their peers. Results indicate that special health care needs prior to school were predictive of lower social-emotional and learning competencies in the early years of school. More research is required into this unique population and their competencies over time. However, the current research provides valuable insight into an under researched 'at risk' population.

Australian children with special health care needs: Social-emotional and learning competencies in the early years

This study examined the relationship between special health care needs and social-emotional and learning competence in the early years, reporting on two waves of data from the Kindergarten Cohort of Growing up in Australia: The Longitudinal Study of Australian Children (LSAC). Six hundred and fifty children were identified through the 2-question Special Health Care Needs Screener as having special health care needs. Children with special health care needs were more likely to be male, to have been of low birth weight, to be taking prescription medications, to be diagnosed with a specific health condition and to be from families where the mother was less well educated. These children scored significantly lower on teacher-rated social-emotional and learning competencies prior to school compared to a control group of children without special health care needs. Multiple regression analyses indicated that being identified with a special health care need prior to school predicted lower social-emotional and learning competencies in the early years of school. Results are discussed in terms of the implications for policy and practice.

Mastery Motivation in Children with Down Syndrome : Promoting and Sustaining Interest in Learning

Motivation is central to children’s learning. Without persistent effort, especially in the face of failure, and an eagerness to engage in challenging tasks, individuals are unlikely to learn as effectively as they might. Because of their cognitive impairments, children with Down syndrome will almost certainly have difficulties with learning. These difficulties will be ameliorated somewhat by strong engagement with learning activities whereas problems with motivation are likely to further jeopardise their academic progress as well as potentially limiting achievements in other areas of life. In this chapter we begin with a general overview of motivation. Using the framework of mastery motivation, we review the relatively small amount of research about children with Down syndrome. We identify the individual characteristics and features of children’s environments that are likely to be related to lower or higher levels of mastery motivation. In the final section, we consider implications for educators and then draw together the findings to provide a set of recommendations for future research.

Strengthening Social and Emotional Learning in Children with Special Needs

Locally and globally, guiding children’s social and emotional development is no longer optional for educators. Research undertaken over the last 20 years provides compelling evidence that early and ongoing development of socio-emotional skills contributes to an individual’s overall health, wellbeing and competence throughout life. Moreover, competence in this domain is now recognised as fundamental to school readiness, school adjustment and academic achievement. As a consequence, social and emotional learning (SEL) is an important theme in current educational policy, curriculum frameworks and classroom practice. This chapter focuses on a particular group of vulnerable learners – children with special needs – and highlights key strategies for educators to use in their everyday classroom practices to strengthen SEL in children from early years through to the end of primary school.

Pattern of learning disabilities in children with extremely low birth weight and broadly average intelligence

To examine the prevalence and pattern of specific areas of learning disability (LD) in neurologically normal children with extremely low birth weight (ELBW) (<or = 800 g) who have broadly average intelligence compared with full-term children with normal birth weight of comparable sociodemographic background, and to explore concurrent cognitive correlates of the specific LDs.

Mental health needs of children and youth with learning disabilities:Overview of a community needs assessment

The purpose of the present report is to describe a community needs assessment that puts the process and choice of a suitable approach into a context. The study examined the mental health needs of children and youth with learning disabilities and their families and how they fit within the continuum of services in Metropolitan Toronto. A series of recommendations was developed for the Ministry of Community and Social Services. The recommendations emphasize: prevention, training and consultation, and research. The study illustrates the importance of involving relevant constituencies in both the planning of a needs assessment and the formulation and implementation of recommendations based on the investigation.

Human Computer Interaction using gestures for mobile devices and serious games: A review

The Human-Computer Interaction (HCI) with interfaces is an active challenge field in the industry over the past decades and has opened the way to communicate with the means of verbal, hand and body gestures using the latest technologies for a variety of different applications in areas such as video games, training and simulation. However, accurate recognition of gestures is still a challenge. In this paper, we review the basic principles and current methodologies used for collecting the raw gesture data from the user for recognize actions the users perform and the technologies currently used for gesture-HCI in games enterprise. In addition, we present a set of projects from various applications in games industry that are using gestural interaction.

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Interviewing children and young people with learning disabilities: guidelines for researchers and multi-professional practice

We argue that it is important for researchers and service providers to not only recognize the rights of children and young people with learning disabilities to have a ‘voice’, but also to work actively towards eliciting views from all. A set of guidelines for critical self-evaluation by those engaged in systematically collecting the views of children and young people with learning disabilities is proposed. The guidelines are based on a series of questions concerning: research aims and ethics (encompassing access/gatekeepers; consent/assent; confidentiality/anonymity/secrecy, recognition, feedback and ownership; and social responsibility) sampling, design and communication