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

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

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An important component of our research however is to find redundancies in the set of bio-signals which may allow for less complicated sensor arrangements to be developed. It may therefore for example be conceivable to derive information about the HR from the BVP instead of the ECG and its more inconvenient sensor arrangement. Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System 4 41 Data Collection Experiments Having established a set of signals which may be of use for recognising emotion, it is then necessary to define a methodology in order to enable the system to translate the signals coming from these sensors into specific emotions.

In: Proc. : Desperately seeking emotions or: Actors, wizards, and human beings. In: Proc. : The Measurement of Meaning. : Emotional space improves emotion recognition. In: Proc. ICSLP. : Emotion recognition in speech signal. html. : Learning with Kernels. MIT Press (2002) Meyer, Schützwohl, Reisenzein: Einführung in die Emotionspsychologie. : Classifying emotions in speech: A comparison of methods. In: Proc. of Eurospeech. : Pattern Classification. 2nd edn. : Integrated bias removal techniques for robust speech recognition.

The role of voice quality in communicating emotion, mood and attitude. Speech Communication 40 (2003) 189–212 20. : Estimation of the glottal pulseform based on discrete all-pole modeling. In: Proceedings of the International Conference on Spoken Language Processing, Yokohama (1994) 1619–1622 24 M. Airas and P. Alku 21. : A method for generating natural-sounding speech stimuli for cognitive brain research. Clinical Neurophysiology 110 (1999) 1329–1333 22. : Amplitude domain quotient of the glottal volume velocity waveform estimated by inverse filtering.

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