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  • 2015

  • Schroder, Marc; Bevacqua, Elisabetta; Cowie, Roddy; Eyben, Florian; Gunes, Hatice; Heylen, Dirk; ter Maat, Mark; McKeown, Gary; Pammi, Sathish; Pantic, Maja; Pelachaud, Catherine; Schuller, Bjorn; Sevin, Etienne de; Valstar, Michel; Wollmer, Martin (2015) : Building autonomous sensitive artificial listeners (Extended abstract): 2015 International Conference on Affective Computing and Intelligent Interaction (ACII 2015): Xi'an, China, 21 - 24 September 2015: Piscataway, NJ: IEEE, S. 456-462

    Abstract: This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and non-verbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and non-verbal behaviours required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and non-verbal behaviour, since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on non-verbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling etc. We first summarise three prototype versions of the SAL scenario, in which the behaviour of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analysing and synthesising the respective behaviours. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behaviour, dialogue management, and synthesis of speaker and listener behaviour of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse, and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.

    Keywords: deutsche Community, Emotionserkennung, Emotionssensitive Systeme, Favoriten, Interaktion, Künstliche Intelligenz, Mensch-Technik-Relationen (MTR), Nonverbal communication, Nonverbale Kommunikation, Realtechnik, Sensitive Artificial Listener, Sozial Intelligente Agenten, Soziale Robotik, Soziosensitive Systeme, Sprachverstehen, Technik, Virtual Agents SAL

  • 2008

  • Batliner, Anton; Steidl, Stefan; Hacker, Christian; Nöth, Elmar (2008): Private emotions versus social interaction. A data-driven approach towards analysing emotion in speech. In: User Modeling and User-Adapted Interaction 18 (1-2), S. 175-206. DOI: 10.1007/s11257-007-9039-4

    Abstract: The 'traditional' first two dimensions in emotion research are VALENCE and AROUSAL. Normally, they are obtained by using elicited, acted data. In this paper, we use realistic, spontaneous speech data from our 'AIBO' corpus (human-robot communication, children interacting with Sony's AIBO robot). The recordings were done in a Wizard-of-Oz scenario: the children believed that AIBO obeys their commands; in fact, AIBO followed a fixed script and often disobeyed. Five labellers annotated each word as belonging to one of eleven emotion-related states; seven of these states which occurred frequently enough are dealt with in this paper. The confusion matrices of these labels were used in a Non-Metrical Multi-dimensional Scaling to display two dimensions; the first we interpret as VALENCE, the second, however, not as AROUSAL but as INTERACTION, i.e., addressing oneself (angry, joyful) or the communication partner (motherese, reprimanding). We show that it depends on the specifity of the scenario and on the subjects' conceptualizations whether this new dimension can be observed, and discuss impacts on the practice of labelling and processing emotional data. Two-dimensional solutions based on acoustic and linguistic features that were used for automatic classification of these emotional states are interpreted along the same lines.

    Keywords: AIBO, annotation, Arousal, categories, Communication, data-driven, deutsche Community, dimensions, emotion, emotion research, Emotionserkennung, Emotionssensitive Systeme, Interaction, Interaktionspartner, Kognitionswissenschaft/Social Sciences/Humanities, Kommunikation, Mensch oder Maschine, Mensch-Technik-Relationen (MTR), non-metrical multi-dimensional scaling, Psychologie, Rolle von Emotion, Realtechnik, script, Soziosensitive Systeme, speech, Technik, Valence
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