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2018
Yamamoto, K.; Inoue, K.; Nakamura, S.; Takanashi, K.; Kawahara, T. (2018) : Dialogue Behavior Control Model for Expressing a Character of Humanoid Robots: 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC): 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC): Hawaii, United States: IEEE, S. 1732-1737

DOI: https://doi.org/10.23919/APSIPA.2018.8659624

Abstract: This paper addresses character expression for humanoid robots that play a social role via spoken dialogue so that the character matches to the given social role such as a lab guide or a counselor. While conventional methods of character expression mostly focused on changing the style of utterance texts, this study focuses on dialogue behavior features that may affect the impression of spoken dialogue. Specifically, we use five dialogue behavior features: utterance amount, backchannel frequency, backchannel variety, filler frequency, and switching pause length (the time until the system responds). We adopt three character traits of extroversion, emotional instability, and politeness for character expression. We then investigate the relationship between the dialogue behavior features and the character traits by conducting subjective evaluations. A statistical analysis of the subjective evaluations shows that the dialogue behavior features except for the backchannel variety are related to either of the character traits. By using the subjective evaluation scores on the relevant traits, we can train models to control the dialogue behavior features of a robot according to the desired character. Another experimental evaluation demonstrates the feasibility of character expression with regard to the traits of extroversion and politeness.

Keywords: Analytical models, character traits, dialogue behavior control model, dialogue behavior features, emotional & politeness, Frequency control, Humanoid Robots, ieee xplore, interactive systems, Mobile robots, paper addresses character expression, PSYCHOLOGY, Social robotic, speech processing, spoken dialogue, Statistical Analysis, Switches, Task Analysis
2017
Bastianelli, Emanuele (2017): Structured learning for spoken language understanding in human-robot interaction. In: INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 36 (5-7), S. 660-683

Abstract: Robots are slowly becoming a part of everyday life, being marketed for commercial applications such as telepresence, cleaning or entertainment. Thus, the ability to interact via natural language with non-expert users is becoming a key requirement. Even if user utterances can be efficiently recognized and transcribed by automatic speech recognition systems, several issues arise in translating them into suitable robotic actions and most of the existing solutions are strictly related to a specific scenario. In this paper, we present an approach to the design of natural language interfaces for human robot interaction, to translate spoken commands into computational structures that enable the robot to execute the intended request. The proposed solution is achieved by combining a general theory of language semantics, i.e. frame semantics, with state-of-the-art methods for robust spoken language understanding, based on structured learning algorithms. The adopted data driven paradigm allows the development of a fully functional natural language processing chain, that can be initialized by re-using available linguistic tools and resources. In addition, it can be also specialized by providing small sets of examples representative of a target newer domain. A systematic benchmarking resource, in terms of a rich and multi-layered spoken corpus has also been created and it has been used to evaluate the natural language processing chain. Our results show that our processing chain, trained with generic resources, provides a solid baseline for command understanding in a service robot domain. Moreover, when domain-dependent resources are provided to the system, the accuracy of the achieved interpretation always improves.

Keywords: Algorithmen, doppelter Treffer, frame semantics, Frame-Semantik, Interaktion, Korpus, Mensch-Maschine-Kommunikation, Mensch-Technik-Interaktion, Mensch-Technik-Relationen (MTR), narürliche Sprache, natural language interface, Observablen/Kriterien für sozial angemessenes Verhalten und dessen Bewertung, Realtechnik, Robotik, Serviceroboter, Soziale Angemessenheit, Soziale Robotik, speech processing, Sprache, Sprachgebrauch, Sprachverarbeitung, Sprachverstehen, Status / biologische Marker, structured learning algorithm, Technik
Burgoon, Judee K.; Magnenat-Thalmann, Nadia; Pantic, Maja (Hg.) (2017): Social signal processing. Cambridge: Cambridge University Press

Abstract: Social Signal Processing is the first book to cover all aspects of the modeling, automated detection, analysis, and synthesis of nonverbal behavior in human-human and human-machine interactions. Authoritative surveys address conceptual foundations, machine analysis and synthesis of social signal processing, and applications. Foundational topics include affect perception and interpersonal coordination in communication; later chapters cover technologies for automatic detection and understanding such as computational paralinguistics and facial expression analysis and for the generation of artificial social signals such as social robots and artificial agents. The final section covers a broad spectrum of applications based on social signal processing in healthcare, deception detection, and digital cities, including detection of developmental diseases and analysis of small groups. Each chapter offers a basic introduction to its topic, accessible to students and other newcomers, and then outlines challenges and future perspectives for the benefit of experienced researchers and practitioners in the field.

Keywords: Computer vision, Computerwissenschaft, Disziplin, doppelter Treffer, Favoriten, Human behaviour analysis', Interaktion, Mensch-Technik-Relationen (MTR), Modelle/Theorien, Realtechnik, Sammelband, Signal, Signale, Signaling social preferences/ Social signal processing, Social interactions, social signal processing, Social Signal Processing (SSP), social signalling, Social signals, Soziale Angemessenheit, soziale Kognition, Soziosensitive Systeme, speech processing, Technik
2009
Picard, Rosalind W. (2009) : Robots with emotional intelligence: 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI): 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI): New York, NY, US: Association for Computing Machinery

DOI: https://doi.org/10.1145/1514095.1514098

Abstract: This keynote talk will illustrate a basic set of skills of emotional intelligence, how they are important for robots and agents that interact with people, and how our research at MIT addresses part of the problem of giving robots such skills. One of the most important skills is the ability to perceive and understand expressions of emotion, which I will highlight by demonstrating new technologies developed to read joint facial-head movements in real-time and associate these with complex affective-cognitive states, and technologies to read paralinguistic vocal cues from speech. I will also show some non-traditional ways robots might sense and learn about human emotion, and ways they can respond to what they sense that can help or hurt people. I will discuss social and ethical issues these technologies raise. Finally, I will present some new possibilities for robots to both learn from people and help teach skills of emotional intelligence to people, especially to those with nonverbal learning impairments who often want to learn these skills, including many people with diagnoses of autism spectrum disorders such as Aspergers Syndrome.

Keywords: Affective computing, affective-cognitive state, Artificial intelligence, Aspergers Syndrome, Autism, Autism spectrum disorder, behavioural sciences, computer aided instruction, deception detection, Educational robots, emotion expression, emotion perception, emotion recognition, emotion understanding, Emotional Intelligence, empathic technology, ethical aspects, ethical issue, facial expression recognition, facial-head movement, human emotion learning, human emotion sensing, human-robot interaction, ieee xplore, image motion analysis, intelligent robots, Laboratories, learning (artificial intelligence), Media, medical disorders, MIT, Moral & Ethik, nonverbal learning impairment, paralinguistic vocal cue, physiological sensing, prosody analysis, robot emotional intelligence, Robot sensing systems, skills teaching, social issue, speech processing, system, Teaching

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