Catálogo de publicaciones - libros

The emotional prosody is multi-dimensional. A debated question is to understand if some parameters are more specialized to convey some emotion dimensions. Selected stimuli, expressing anxiety, disappointment, disgust, disquiet, joy, resignation, satisfaction and sadness, were extracted from the acted part of a French corpus supposed to include only variations of direct emotional expressions. These stimuli were used as a basis for the synthesis of artefactual stimuli integrating the emotional contour of each prosodic parameter separately, which were evaluated on a perceptual experiment. Results indicate that (1) no parameter alone is able to carry the whole emotion information, (2) F0 contours (not only the global F0 value) reveal to bring more information on positive expressions, (3) voice quality and duration convey more information on negative expressions, and (4) the intensity contours do not bring any significant information when used alone.

In simple cases, affective computing is a computational device recognizing and acting upon the emotions of its user or having (or simulating having) emotions of its own in complex cases. Multimodal technology is currently one of the hottest focuses in affective computing research. However, the lack of a large-scale multimodal database limits the research to some respective and scattered fields, such as affective recognition by video or by audio. This paper describes the development and implementation of an XML-based multimodal affective annotation system which is called MAAS (Multimodal Affective Annotation System). MAAS contains a hierarchical affective annotation model based on the 3-dimensional affect space derived from Mehrabian’s PAD temperament scale. The final annotation file is formed in XML format in order to interchange the resources with other research groups conveniently.

Affective database plays an important role in the process of affective computing which has been an attractive field of AI research. Based on analyzing current databases, a Chinese affective database (CHAD) is designed and established for seven emotion states: neutral, happy, sad, fear, angry, surprise and disgust. Instead of choosing the personal suggestion method, audiovisual materials are collected in four ways including three types of laboratory recording and movies. Broadcast programmes are also included as source of vocal corpus. By comparison of the five sources two points are gained. First, although broadcast programmes get the best performance in listening experiment, there are still problems as copyright, lacking visual information and can not represent the characteristics of speech in daily life. Second, laboratory recording using sentences with appropriately emotional content is an outstanding source of materials which has a comparable performance with broadcasts.

The design of affective interfaces such as credible expressive characters in story-telling applications requires the understanding and the modeling of relations between realistic emotions and behaviors in different modalities such as facial expressions, speech, hand gestures and body movements. Yet, research on emotional multimodal behaviors has focused on individual modalities during acted basic emotions. In this paper we describe the coding scheme that we have designed for annotating multimodal behaviors observed during mixed and non acted emotions. We explain how we used it for the annotation of videos from a corpus of emotionally rich TV interviews. We illustrate how the annotations can be used to compute expressive profiles of videos and relations between non basic emotions and multimodal behaviors.

This paper presents a multimodal database developed within the EU-funded project MYSELF. The project aims at developing an e-learning platform endowed with affective computing capabilities for the training of relational skills through interactive simulations. The database includes data coming from 34 participants and concerning physiological parameters, vocal nonverbal features, facial expression and posture. Ten different emotions were considered (anger, joy, sadness, fear, contempt, shame, guilt, pride, frustration and boredom), ranging from primary to self-conscious emotions of particular relevance in learning process and interpersonal relationships. Preliminary results and analyses are presented, together with directions for future work.

The purpose of this paper is to construct a virtual assistant. Basic emotions theory points out that compound emotion consists of eight prototype basic emotions and “drives” which reflects people’s will. According to this theory, we construct a psychology model. By adjusting parameters in it, we can simulate different human psychologies. Based on this model, combining real-time facial expression and voice recognition and synthesizing technology, we construct a virtual assistant. Proved by experiment, our system obeys human emotion rules.

Previous emotion recognition systems have mainly focused on pattern classification, rather than utilizing sensing technologies or feature extraction methods. This paper introduces a method of physiological sensing and feature extraction for emotion recognition that is based on an oriental medicine approach. The specific points for affective sensing were experimentally determine, in which it was found that skin conductance measurements of the forearm region correlate well with acupuncture spots. Features are then extracted by the same way to interpret pulsation signals in diagnosis. We found that the proposed sensing and feature extraction method benefits the recognition of emotion with a neural network classifier.

The nature of (un)conscious human emotional information processing remains a great mystery. On the one hand, classical models view human conscious emotional information processing as computation among the brain’s neurons but fail to address its enigmatic features. On the other hand, quantum processes (superposition of states, nonlocality, entanglement,) also remain mysterious, yet are being harnessed in revolutionary information technologies like quantum computation, quantum cryptography, and quantum teleportation. In this paper, we would like to discuss several experiments that suggest a special role for unconscious emotional information processing in the human-computer interaction. What are its consequences and could this be the missing link between quantum information theory and conscious human emotional information processing?

The paper first builds a novel affective model based on rough sets, presents the static description of affective space. Meanwhile, the paper creatively combines rough sets with Markov chain, gives the dynamic forecast of human affective change. In this affective model, some concepts and states are defined such as affective description precision and so on. It is a fundamental work to more research. Simulations are done using Matlab software, and simulation results show that this affective model can well simulate the human emotion. The results of this paper are innovative. It is a new research direction of affective computing that rough sets and affective computing infiltrate each other.

A humanistic computer teaching system is presented in this paper. The core of this system is the affective interaction between teacher and student. Based on theories in Psychology and the theory of Artificial Psychology, the emotion-learning model is developed. Four basic emotions and four types of learning psychology state are defined according to the Basic Emotion Theory, and two-dimensional emotion space is designed ground on Dimensional Emotion theory. This system could offer the student’s psychological state and psychological value to the teacher. Finally, this system was realized by using the recognition method that is based on digital image processing technology.