Abstract not found

Emotions are universally recognized from facial expressions—or so it has been claimed. To support that claim, research has been carried out in various modern cultures and in cultures relatively isolated from Western influence. A review of the methods used in that research raises questions of its ecological, convergent, and internal validity. Forced-choice response format, within-subject design, preselected photographs of posed facial expressions, and other features of method are each problematic. When they are altered, less supportive or nonsupportive results occur. When they are combined, these method factors may help to shape the results. Facial expressions and emotion labels are probably associated, but the association may vary with culture and is loose enough to be consistent with various alternative accounts, 8 of which are discussed. "Everyone knows that grief involves a gloomy and joy a cheerful countenance.... There are characteristic facial expressions which are observed to accompany anger, fear, erotic excitement, and all the other passions " (Aristotle, nd/1913, pp. 805, 808). Aristotle was not proposing a new idea but was cataloging what was known on the topic of physiognomy. The theory was that a person's physical appearance, especially in the face, reveals deeper characteristics: Poor proportions reveal a rogue, soft hair a coward, and a smile a happy person.' Today, few psychologists share Aristotle's belief about the meaning of poor proportions or soft hair, but many share his beliefs about facial expression and emotion. Oatley and Jenkins (1992) observed, "By far the most extensive body of data in the field of human emotions is that on facial expressions of emotion" (p. 67). Recent reviews of those data (see Table 1) agree that the face reveals emotion in a way that is universally understood: Happiness, surprise, fear, anger, contempt, disgust, and sadness—these seven emotions, plus or minus two, are recognized from facial expressions by all human beings, regardless of their cultural background.

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

Modern speech synthesis systems with very high intelligibility are readily available in a number of languages. However, the output from all present systems is still readily identifiable as being machine-generated - the output does not sound "natural". One aspect of naturalness is the variability introduced by the emotional state of the speaker, and related pragmatic effects; no current commercial systems include such variation. Comparatively little work has been done to investigate how a speaker's emotional state creates variation in the speech signal, and this work has traditionally been performed by psychologists and has remained distinct from mainstream speech science. Current research suggests that there will be considerable effort involved in producing any accurate description of pragmatic variations in speech, but there has recently been increasing interest in this area due to potential applications in many branches of speech technology.

We present research in synthetic social behavior for interactive virtual characters. We describe a model from the natural world, the gray wolf (Canis lupus), and the social behavior exhibited by packs of wolves, to use as the target for an interactive installation entitled AlphaWolf, which was shown at SIGGRAPH 2001. We offer a computational model that captures a subset of the social behavior of wild wolves, involving models of learning, emotion and development. There is a range of real-world applications of synthetic social behavior, from short-term possibilities such as autonomous characters for computer games, to long-term applications such as computer interfaces that can interact more appropriately with humans by utilizing human social abilities.

In this paper we investigate social tags as a novel highvolume source of semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional semantic space that is extremely well-behaved at the track level, in particular being highly organised by artist and musical genre. We introduce the use of Correspondence Analysis to visualise this semantic space, and show how it can be applied to create a browse-by-mood interface for a psychologically-motivated two-dimensional subspace representing musical emotion. 1

We present a system in which computer-graphical virtual characters may be controlled by a user and also remain “in character. ” The system allows the user to have high-level control over the actions of a character, while the emotional state of the character is autonomously maintained by the computer. We show how this system functioned as part of the AlphaWolf installation, presented in the Emerging Technologies program at SIGGRAPH 2001. Results from a 32-subject human user study support the hypothesis that users could control a character’s actions without sacrificing its realistic autonomous personality. This system is appropriate to the control of computer-graphical entities that are meant to have personalities distinct from those of the humans that direct them.

This study has three purposes: the first is to study if there is consensus in the way listeners interpret different emotions and attitudes expressed by a Swedish speaker, the second is to see if this interpretation is dependent on the listeners ’ cultural and linguistic background, and the third is to ascertain whether there is any reoccurring relation between acoustic and semantic properties of the stimuli. Recordings of a Swedish speaker uttering a phrase while expressing different emotions were interpreted by listeners with different first languages, Swedish, English, Finnish and Spanish, who were to judge the emotional contents of the expressions. The results show that some emotions, e.g. anger, fear, sadness and surprise, were interpreted in accordance with intended emotion to a greater degree than others, which were interpreted as expected to a lesser degree. Emotions were interpreted with different degrees of success depending on the first languages of listeners; native listeners were the most successful. The results suggest that emotions with similar semantic features, e.g. anger and dominance or fear and shyness have similar acoustic features e.g. short duration and strong intensity (anger and dominance) or longer duration and weak intensity (fear and shyness). Key words: Cross-linguistic interpretation, emotion, prosody

We are creating a pack of virtual creatures who exhibit the kinds of social interactions found in a natural species of animal, the gray wolf (Canis lupus). To do this, we are extending our synthetic character building toolkit to enable our characters to have and express emotional states; to form context-specific emotional memories; and to learn to adapt pre-existing behaviors for use in novel social contexts. We describe how these elements combine to form the underpinnings for our interactive installation entitled "AlphaWolf", shown at SIGGRAPH 2001. The computational representations that allow social learning in our virtual wolves demonstrate the intimate connections among social behavior, emotion and learning. These representations are applicable to building a wider range of socially intelligent agents. We hope that our studies of virtual wolves will offer insight into the processes by which real wolves and other animals understand their environments.

Nonverbal communication is an important and often underestimated instrument in social interactions. The paralinguistic elements of speech, which are described in common speech as ”tone of voice”, are one channel of the nonverbal communication. They, together with conversational dynamics, are a very powerful measurement for interactions, without looking at the content of the conversation. A subset of these features, the social signaling measurements, are very useful when analyzing and quantifying conversation. The goal of this thesis is to better understand the framework of social signaling. We applied the social signaling measurements to negotiations as well as to small group interactions. For negotiation we were able to predict up to 30 % of the variance in individual outcome. The use of automated algorithms enables to build real-time feedback mechanisms that can then