Recent neurological studies indicate that the role of emotion in human cognition is essential; emotions are not a luxury. Instead, emotions play a critical role in rational decision-making, in perception, in human interaction, and in human intelligence. These facts, combined with abilities computers are acquiring in expressing and recognizing affect, open new areas for research. This paper defines key issues in "affective computing," computing that relates to, arises from, or deliberately influences emotions. New models are suggested for computer recognition of human emotion, and both theoretical and practical applications are described for learning, human-computer interaction, perceptual information retrieval, creative arts and entertainment, human health, and machine intelligence. Significant potential advances in emotion and cognition theory hinge on the development of affective computing, especially in the form of wearable computers. This paper establishes challenges and future directions for this emerging field.

This paper reviews "socially interactive robots": robots for which social human-robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the di#erent forms of "social robots". We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report[61].

We present a probabilistic model to monitor a user’s emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user’s emotional arousal (i.e., the state of the interaction) and its effects (i.e., bodily expressions that are known to be influenced by emotional reactions). The probabilistic model relies on a Dynamic Decision Network to leverage any indirect evidence on the user’s emotional state, in order to estimate this state and any other related variable in the model. This is crucial in a modeling task in which the available evidence usually varies with the user and with each particular interaction. The probabilistic model we present is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user’s learning and engagement during the interaction with educational games. 2 1.

Abstract—The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. This paper explores the detection of domain-specific emotions using language and discourse information in conjunction with acoustic correlates of emotion in speech signals. The specific focus is on a case study of detecting negative and non-negative emotions using spoken language data obtained from a call center application. Most previous studies in emotion recognition have used only the acoustic information contained in speech. In this paper, a combination of three sources of information—acoustic, lexical, and discourse—is used for emotion recognition. To capture emotion information at the language level, an information-theoretic notion of emotional salience is introduced. Optimization of the acoustic correlates of emotion with respect to classification error was accomplished by investigating different feature sets obtained from feature selection, followed by principal component analysis. Experimental results on our call center data show that the best results are obtained when acoustic and language information are combined. Results show that combining all the information, rather than using only acoustic information, improves emotion classification by 40.7 % for males and 36.4 % for females (linear discriminant classifier used for acoustic information). Index Terms—Acoustic correlates, dialog systems, emotion recognition, emotional salience, feature selection, information fusion, principal component analysis, spoken language processing. I.

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In this paper we review the major approaches to Multimodal Human Computer Interaction, giving an overview of the field from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for Multimodal Human Computer Interaction (MMHCI) research.

This paper presents algorithms that allow a robot to express its emotions by modulating the intonation of its voice. They are very simple and efficiently provide life-like speech thanks to the use of concatenative speech synthesis. We describe a technique which allows to continuously control both the age of a synthetic voice and the quantity of emotions that are expressed. Also, we present the first large-scale data mining experiment about the automatic recognition of basic emotions in informal everyday short utterances. We focus on the speaker-dependent problem. We compare a large set of machine learning algorithms, ranging from neural networks, Support Vector Machines or decision trees, together with 200 features, using a large database of several thousands examples. We show that the difference of performance among learning schemes can be substantial, and that some features which were previously unexplored are of crucial importance. An optimal feature set is derived through the use of a genetic algorithm. Finally, we explain how this study can be applied to real world situations in which very few examples are available. Furthermore, we describe a game to play with a personal robot which facilitates teaching of examples of emotional utterances in a natural and rather unconstrained manner.

Human speech provides a natural and intuitive interface for both communicating with humanoid robots as well as for teaching them. In general, the acoustic pattern of speech contains three kinds of information: who the speaker is, what the speaker said, and how the speaker said it. This paper focuses on the question of recognizing affective communicative intent in robot-directed speech. We present an approach for recognizing four distinct prosodic patterns that communicate praise, prohibition, attention, and comfort to preverbal infants. These communicative intents are well matched to teaching a robot since praise, prohibition, and directing the robot’s attention to relevant aspects of a task, could be used by a human instructor to intuitively facilitate the robot’s learning process. We integrate this perceptual ability into our robot’s ”emotion ” system, thereby allowing a human to directly manipulate the robot’s affective state. This has a powerful organizing influence on the robot’s behavior, and will ultimately be used to socially communicate affective reinforcement. Communicative efficacy has been tested with people very familiar with the robot as well as with naive subjects.

Recent technological advances have enabled human users to interact with comput-ers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities to control the computer such as voice, gesture, and force-feedback are emerging. Among these, voice and vision are two natural modalities in human-to-human communication. Automatic speech recognition (ASR) technology has matured enough to allow users to dictate to a word processor or operate the computer using voice commands. Computer vision techniques have enabled the computer to see. Interacting with comput-ers in these modalities is much more natural for people, and the progression is towards the kind of interaction between humans. Despite these advances, one necessary ingredi-ent for natural interaction is still missing–emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in some applications such as computer-aided learning or user-friendly on-line help. This thesis addresses the problem of detecting human emotional expressions by

The paper describes two experimental studies on vocal emotion expression and recognition. The first study deals with a corpus of 700 short ut terances expressing five emotions: happiness, anger, sadness, fear, and normal (unemotional) state, which were portrayed by thirty non-professional actors. After evaluation a part of this corpus was used for extracting features and training backpropagation neural network models. Some statistics of the pitch, the first and second formants, energy and the speaking rate were selected as relevant features using feature selection techniques. Several neural network recognizers and ensembles of recognizers were created. The recognizers have demonstrated the following accuracy: normal state - 60-75%, happiness -- 6070 %, anger -- 70-80%, sadness -- 70-85%, and fear -- 35-55%. The total average accuracy is about 70%. The second study uses a corpus of 56 telephone messages of varying length (from 15 to 90 seconds) expressing mostly normal and ...