Adolescents’ self-regulation during job interviews through an AI coaching environment

The use of Artificial Intelligence in supporting social skills development is an emerging area of interest in education. This paper presents work which evaluated the impact of a situated experience coupled with open learner modelling on 16–18 years old learners’ verbal and non-verbal behaviours during job interviews with AI recruiters. The results revealed significantly positive trends on certain aspects of learners’ verbal and non-verbal performance and on their self-efficacy

Speaker-independent negative emotion recognition

This work aims to provide a method able to distinguish between negative and non-negative emotions in vocal interaction. A large pool of 1418 features is extracted for that purpose. Several of those features are tested in emotion recognition for the first time. Next, feature selection is applied separately to male and female utterances. In particular, a bidirectional Best First search with backtracking is applied. The first contribution is the demonstration that a significant number of features, first tested here, are retained after feature selection. The selected features are then fed as input to support vector machines with various kernel functions as well as to the K nearest neighbors classifier. The second contribution is in the speaker-independent experiments conducted in order to cope with the limited number of speakers present in the commonly used emotion speech corpora. Speaker-independent systems are known to be more robust and present a better generalization ability than the speaker-dependent ones. Experimental results are reported for the Berlin emotional speech database. The best performing classifier is found to be the support vector machine with the Gaussian radial basis function kernel. Correctly classified utterances are 86.73%±3.95% for male subjects and 91.73%±4.18% for female subjects. The last contribution is in the statistical analysis of the performance of the support vector machine classifier against the K nearest neighbors classifier as well as the statistical analysis of the various support vector machine kernels impact. © 2010 IEEE

Using system and user performance features to improve emotion detection in spoken tutoring dialogs

In this study, we incorporate automatically obtained system/user performance features into machine learning experiments to detect student emotion in computer tutoring dialogs. Our results show a relative improvement of 2.7% on classification accuracy and 8.08% on Kappa over using standard lexical, prosodie, sequential, and identification features. This level of improvement is comparable to the performance improvement shown in previous studies by applying dialog acts or lexical/prosodic-/discourse- level contextual features

Acoustic and Prosodic Correlates of Social Behavior

We describe acoustic/prosodic and lexical correlates of social variables annotated on a large corpus of task-oriented spontaneous speech. We employ Amazon Mechanical Turk to label the corpus with a large number of social behaviors, examining results of three of these here. We find significant differences between male and female speakers for perceptions of attempts to be liked, likeability, speech planning, that also differ depending upon the gender of their conversational partners

Music emotion identification from lyrics

ABSTRACT-Very large online music databases have recently been created by vendors, but they generally lack content-based retrieval methods. One exception is Allmusic.com which offers browsing by musical emotion, using human experts to classify several thousand songs into 183 moods. In this paper, machine learning techniques are used instead of human experts to extract emotions in Music. The classification is based on a psychological model of emotion that is extended to 23 specific emotion categories. Our results for mining the lyrical text of songs for specific emotion are promising, generate classification models that are human-comprehensible, and generate results that correspond to commonsense intuitions about specific emotions. Mining lyrics focused in this paper is one aspect of research which combines different classifiers of musical emotion such as acoustics and lyrical text

Emotions and Strategies for Preparation of Emotional Speech Database

Abstract The exploration of how we as human beings react to the world and interact with it and each other remains one of the greatest challenges. The ability to recognize emotional states of a person perhaps the most important for successful inter personal social interaction. Automatic emotional speech recognition system can be characterized by the used features, the investigated emotional categories, the methods to collect speech utterances, the languages and the type of the classifier used in the experiment. Since a well defined database is the necessary precondition for improving the performance Automatic emotional speech recognition systems. This paper explores the theories that explain the social and cognitive roles of emotions and mental states and their expression in human behaviors and communication. The paper describes the planning and accomplishment of a native language emotional speech database of acted emotional speech by number of speakers, recording strategies, conversion etc as well as the alternative approach is briefly addressed. Such database would also contribute to research in intonation and emotion

Perception of Suspense in Live Football Commentaries from German and British Perspectives

Kern F, Trouvain J, Samlowski B. Perception of Suspense in Live Football Commentaries from German and British Perspectives. SpeechProsody 2018-8. 2018