Laughter Type Recognition from Whole Body Motion

Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

Human Perception of Laughter from Context-free Whole Body Motion Dynamic Stimuli

Laughter is a ubiquitous social signal in human interactions yet it remains understudied from a scientific point of view. The need to understand laughter and its role in human interactions has become more pressing as the ability to create conversational agents capable of interacting with humans has come closer to a reality. This paper reports on three aspects of the human perception of laughter when context has been removed and only the body information from the laughter episode remains. We report on ability to categorise the laugh type and the sex of the laugher; the relationship between personality factors with laughter categorisation and perception; and finally the importance of intensity in the perception and categorisation of laughter.

The Belfast Storytelling Database: A spontaneous social interaction database with laughter focused annotation

To support the endeavor of creating intelligent interfaces between computers and humans the use of training materials based on realistic human-human interactions has been recognized as a crucial task. One of the effects of the creation of these databases is an increased realization of the importance of often overlooked social signals and behaviours in organizing and orchestrating our interactions. Laughter is one of these key social signals; its importance in maintaining the smooth flow of human interaction has only recently become apparent in the embodied conversational agent domain. In turn, these realizations require training data that focus on these key social signals. This paper presents a database that is well annotated and theoretically constructed with respect to understanding laughter as it is used within human social interaction. Its construction, motivation, annotation and availability are presented in detail in this paper.

Turing’s Menagerie: Talking Lions, Virtual Bats, Electric Sheep and Analogical Peacocks: Common Ground and Common Interest are Necessary Components of Engagement

This theoretical paper attempts to define some of the key components and challenges required to create embodied conversational agents that can be genuinely interesting conversational partners. Wittgenstein’s argument concerning talking lions emphasizes the importance of having a shared common ground as a basis for conversational interactions. Virtual bats suggests that–for some people at least–it is important that there be a feeling of authenticity concerning a subjectively experiencing entity that can convey what it is like to be that entity. Electric sheep reminds us of the importance of empathy in human conversational interaction and that we should provide a full communicative repertoire of both verbal and non-verbal components if we are to create genuinely engaging interactions. Also we may be making the task more difficult rather than easy if we leave out non-verbal aspects of communication. Finally, analogical peacocks highlights the importance of between minds alignment and establishes a longer term goal of being interesting, creative, and humorous if an embodied conversational is to be truly an engaging conversational partner. Some potential directions and solutions to addressing these issues are suggested.

Configuration et exploitation d'une machine émotionnelle

Dans ce travail, nous explorons la faisabilité de doter les machines de la capacité de prédire, dans un contexte d'interaction homme-machine (IHM), l'émotion d'un utilisateur, ainsi que son intensité, de manière instantanée pour une grande variété de situations. Plus spécifiquement, une application a été développée, appelée machine émotionnelle, capable de «comprendre» la signification d'une situation en se basant sur le modèle théorique d'évaluation de l'émotion Ortony, Clore et Collins (OCC). Cette machine est apte, également, à prédire les réactions émotionnelles des utilisateurs, en combinant des versions améliorées des k plus proches voisins et des réseaux de neurones. Une procédure empirique a été réalisée pour l'acquisition des données. Ces dernières ont fourni une connaissance consistante aux algorithmes d'apprentissage choisis et ont permis de tester la performance de la machine. Les résultats obtenus montrent que la machine émotionnelle proposée est capable de produire de bonnes prédictions. Une telle réalisation pourrait encourager son utilisation future dans des domaines exploitant la reconnaissance automatique de l'émotion.

Supporting emotion communication in information systems

Society is becoming increasingly reliant on Information Systems to meet its everyday communication requirements, yet many current implementations lack support for important conversational cues. One such cue is emotion communication. Emotion communication carries with it many signals that affect our behaviour, the interpretation of the message and provide a catalyst to other forms of communication such as empathy and the formation of social ties. Emotion itself can affect the very decision to communicate, or the way in which one may respond to a given communication. To explore the ways in which systems may better support emotion communication between members of a social group, a cloud-based information system was developed and trialled which both large and small groups. This paper presents results on how Information Systems can best support emotion communication in social groups.

Using an animated pedagogical agent to interact affectively with the student

This work proposes an animated pedagogical agent that has the role of providing emotional support to the student: motivating and encouraging him, making him believe in his self-ability, and promoting a positive mood in him, which fosters learning. This careful support of the agent, its affective tactics, is expressed through emotional behaviour and encouragement messages of the lifelike character. Due to human social tendency of anthropomorphising software, we believe that a software agent can accomplish this affective role. In order to choose the adequate affective tactics, the agent should also know the student’s emotions. The proposed agent recognises the student’s emotions: joy/distress, satisfaction/disappointment, anger/gratitude, and shame, from the student’s observable behaviour, i. e. his actions in the interface of the educational system. The inference of emotions is psychologically grounded on the cognitive theory of emotions. More specifically, we use the OCC model which is based on the cognitive approach of emotion and can be computationally implemented. Due to the dynamic nature of the student’s affective information, we adopted a BDI approach to implement the affective user model and the affective diagnosis. Besides, in our work we profit from the reasoning capacity of the BDI approach in order for the agent to deduce the student’s appraisal, which allows it to infer the student’s emotions. As a case study, the proposed agent is implemented as the Mediating Agent of MACES: an educational collaborative environment modelled as a multi-agent system and pedagogically based on the sociocultural theory of Vygotsky.

Avaliação da influência de emoções na tomada de decisão de sistemas computacionais.

Este trabalho avalia a influência das emoções humanas expressas pela mímica da face na tomada de decisão de sistemas computacionais, com o objetivo de melhorar a experiência do usuário. Para isso, foram desenvolvidos três módulos: o primeiro trata-se de um sistema de computação assistiva - uma prancha de comunicação alternativa e ampliada em versão digital. O segundo módulo, aqui denominado Módulo Afetivo, trata-se de um sistema de computação afetiva que, por meio de Visão Computacional, capta a mímica da face do usuário e classifica seu estado emocional. Este segundo módulo foi implementado em duas etapas, as duas inspiradas no Sistema de Codificação de Ações Faciais (FACS), que identifica expressões faciais com base no sistema cognitivo humano. Na primeira etapa, o Módulo Afetivo realiza a inferência dos estados emocionais básicos: felicidade, surpresa, raiva, medo, tristeza, aversão e, ainda, o estado neutro. Segundo a maioria dos pesquisadores da área, as emoções básicas são inatas e universais, o que torna o módulo afetivo generalizável a qualquer população. Os testes realizados com o modelo proposto apresentaram resultados 10,9% acima dos resultados que usam metodologias semelhantes. Também foram realizadas análises de emoções espontâneas, e os resultados computacionais aproximam-se da taxa de acerto dos seres humanos. Na segunda etapa do desenvolvimento do Módulo Afetivo, o objetivo foi identificar expressões faciais que refletem a insatisfação ou a dificuldade de uma pessoa durante o uso de sistemas computacionais. Assim, o primeiro modelo do Módulo Afetivo foi ajustado para este fim. Por fim, foi desenvolvido um Módulo de Tomada de Decisão que recebe informações do Módulo Afetivo e faz intervenções no Sistema Computacional. Parâmetros como tamanho do ícone, arraste convertido em clique e velocidade de varredura são alterados em tempo real pelo Módulo de Tomada de Decisão no sistema computacional assistivo, de acordo com as informações geradas pelo Módulo Afetivo. Como o Módulo Afetivo não possui uma etapa de treinamento para inferência do estado emocional, foi proposto um algoritmo de face neutra para resolver o problema da inicialização com faces contendo emoções. Também foi proposto, neste trabalho, a divisão dos sinais faciais rápidos entre sinais de linha base (tique e outros ruídos na movimentação da face que não se tratam de sinais emocionais) e sinais emocionais. Os resultados dos Estudos de Caso realizados com os alunos da APAE de Presidente Prudente demonstraram que é possível melhorar a experiência do usuário, configurando um sistema computacional com informações emocionais expressas pela mímica da face.

Influencing the perceived emotions of music with intent

Music is an immensely powerful affective medium that pervades our everyday life. With ever advancing technology, the reproduction and application of music for emotive and information transfer purposes has never been more prevalent. In this paper we introduce a rule-based engine for influencing the perceived emotions of music. Based on empirical music psychology, we attempt to formalise the relationship between musical elements and their perceived emotion. We examine the modification to structural aspects of music to allow for a graduated transition between perceived emotive states. This engine is intended to provide music reproduction systems with a finer grained control over this affective medium; where perceived musical emotion can be influenced with intent. This intent comes from both an external application and the audience. Using a series of affective computing technologies, an audience’s response metrics and attitudes can be incorporated to model this intent. A generative feedback loop is set up between the external application, the influencing process and the audience’s response to this, which together shape the modification of musical structure. The effectiveness of our rule system for influencing perceived musical emotion was examined in earlier work, with a small test study providing generally encouraging results.

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

The Booth: Bringing Out the Super Hero in You

Demo paper about the booth

Building autonomous sensitive artificial listeners (Extended abstract)

This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and non-verbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and non-verbal behaviours required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and non-verbal behaviour, since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on non-verbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling etc. We first summarise three prototype versions of the SAL scenario, in which the behaviour of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analysing and synthesising the respective behaviours. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behaviour, dialogue management, and synthesis of speaker and listener behaviour of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse, and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.