Affective Dialogue Systems by Andri E. (Ed), Dybkjaer L. (Ed), Minker W. (Ed)

By Andri E. (Ed), Dybkjaer L. (Ed), Minker W. (Ed)

This publication constitutes the refereed lawsuits of the foreign instructional and learn Workshop on Affective discussion platforms, advertisements 2004, held in Kloster Irsee, Germany in June 2004.The 21 revised complete papers and 14 revised brief papers awarded have been rigorously reviewed and chosen for presentation. The papers are geared up in topical sections on emotion reputation; affective consumer modeling; emotional databases, annotation schemes, and instruments; affective conversational brokers and discussion structures; synthesis of emotional speech and facial animations; affective tutoring platforms; assessment of affective discussion structures; and demonstrations.

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027). The range of NAQ values for joy was larger than for neutral, however. The NAQ means for females did not exhibit this similarity. While the total NAQ mean was higher for females than for males, in angriness and neutral emotion the NAQ means were smaller for females than for males. This indicates sharper glottal pulses in angriness and neutral emotion for females than for males. Analysis of variance (ANOVA) was used to study the sources of variability in the NAQ value data. Table 2 shows the results of the analysis of emotion, gender and recitation number with regard to the effects on NAQ value.

An ideal solution as a starting point proved to be the IAPS photo set [2,3]. This takes the form of over 800 photographs which have been classified by a large number of participants in terms of arousal and valence. Arousal refers to how strong the picture content is. Valence (sometimes known as ‘pleasure’) refers to how positive or negative the content is considered to be. The IAPS photoset therefore possessed a number of advantages from the outset in that it was validated, and classified in terms of two simple but relevant dimensions.

The results obtained with the AIBO corpus are very similar to the results with the feature set from [13] (tables 2 and 9). 32 5 D. Küstner et al. Environmental Aspects No real life applications are described in the approaches to emotion recognition from speech, that appeared recently. While investigating such an application, one faces several “real world problems” which are determined by environmental influences. A demo system has been built, in which the feature calculation and the trained classifiers are implemented.

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