poliTE-Literatur-Datenbank

McColl, Derek; Hong, Alexander; Hatakeyama, Naoaki; Nejat, Goldie; Benhabib, Beno (2016): A survey of autonomous human affect detection methods for social robots engaged in natural HRI. In: Journal of Intelligent & Robotic Systems 82 (1), S. 101-133. DOI: 10.1007/s10846-015-0259-2

DOI: https://doi.org/10.1007/s10846-015-0259-2

Abstract: In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world human-centered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Keywords: affect classification models, Affective Valence, automated affect detection, Body language, FACIAL EXPRESSIONS, Human Computer Interaction, human-robot interactions, Mensch-Roboter-Interaktion, Mensch-Technik-Relation, multi-modal, physiological signals, physiology, Social intelligence, Soziale Intelligenz, VOICE

Thompson, William Forde; Balkwill, Laura-Lee (2006): Decoding speech prosody in five languages. In: Semiotica (158). DOI: 10.1515/SEM.2006.017

Abstract: Twenty English-speaking listeners judged the emotive intent of utterances spoken by male and female speakers of English, German, Chinese, Japanese, and Tagalog. The verbal content of utterances was neutral but prosodic elements conveyed each of four emotions: joy, anger, sadness, and fear. Identification accuracy was above chance performance levels for all emotions in all languages. Across languages, sadness and anger were more accurately recognized than joy and fear. Listeners showed an in-group advantage for decoding emotional prosody, with highest recognition rates for English utterances and lowest recognition rates for Japanese and Chinese utterances. Acoustic properties of stimuli were correlated with the intended emotion expressed. Our results support the view that emotional prosody is decoded by a combination of universal and culture-specific cues.

Keywords: Communication, CULTURAL-DIFFERENCES, culture specific, emotion recognition, FACIAL EXPRESSIONS, Japanese, JUDGMENTS, MUSIC PERFORMANCE, PERCEPTION, Status / biologische Marker, Stimme / Prosodie, VOCAL EXPRESSION, VOICE

Belin, Pascal; Fecteau, Shirley; Bédard, Catherine (2004): Thinking the voice: neural correlates of voice perception. In: Trends in cognitive sciences 8 (3), S. 129-135. DOI: 10.1016/j.tics.2004.01.008

DOI: https://doi.org/10.1016/j.tics.2004.01.008

Abstract: The human voice is the carrier of speech, but also an "auditory face" that conveys important affective and identity information. Little is known about the neural bases of our abilities to perceive such paralinguistic information in voice. Results from recent neuroimaging studies suggest that the different types of vocal information could be processed in partially dissociated functional pathways, and support a neurocognitive model of voice perception largely similar to that proposed for face perception.

Keywords: Auditory Perception/physiology, Brain Mapping, Cerebral Cortex/physiology, Concept Formation/physiology, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Interpersonal Relations, Magnetic Resonance Imaging, Positron-Emission Tomography, Sound Spectrography, Speech Acoustics, Speech Perception/physiology, VOICE