DualKeepon: a human–robot interaction testbed to study linguistic features of speech (original) (raw)
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Despite recent developments in integrating autonomous and human-like robots into many aspects of everyday life, social interactions with robots are still a challenge. Here, we focus on a central tool for social interaction: verbal communication. We assess the extent to which humans co-represent (simulate and predict) a robot's verbal actions. During a joint picture naming task, participants took turns in naming objects together with a social robot (Pepper, Softbank Robotics). Previous findings using this task with human partners revealed internal simulations on behalf of the partner down to the level of selecting words from the mental lexicon, reflected in partner-elicited inhibitory effects on subsequent naming. Here, with the robot, the partner-elicited inhibitory effects were not observed. Instead, naming was facilitated, as revealed by faster naming of word categories co-named with the robot. This facilitation suggests that robots, unlike humans, are not simulated down to the level of lexical selection. Instead, a robot's speaking appears to be simulated at the initial level of language production where the meaning of the verbal message is generated, resulting in facilitated language production due to conceptual priming. We conclude that robots facilitate core conceptualization processes when humans transform thoughts to language during speaking. Recent developments in artificial intelligence have introduced autonomous and human-like robots into numerous aspects of everyday life. Natural social interactions with robots are however still far from expectations, emphasizing the need to advance human-robot social interaction as one of the currently most pressing challenges of the field of robotics 1. In this study we focus on the increasingly more prevalent domain of interaction with robots: verbal communication 2,3. We assess the extent to which a social robot's verbal actions, in social interaction with humans, are simulated and predicted, or in other words co-represented, and explore the consequences of robot verbal co-representation on human language production. We focus on a social humanoid robot (Pepper, Softbank Robotics). Social robots, as physical agents, in contrast to other robots (e.g. service robots), have been developed specifically for interaction with humans 4 .
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We present here a system capable of learning to extract the correct comprehension and production of personal pronouns and proper nouns during Human-Robot or Human-Human interactions. We use external 3D spatial and acoustic sensors with the robot iCub to allow the system to learn the proper mapping between different pronouns and names to their properties in different interaction contexts. The properties are Subject (Su), Speaker (Sp), Addressee (Ad) and Agent (Ag). A fast mapping system is used to extract correlation between the different properties. After a learning phase, the robot is able to find the missing property when only 3 out of 4 are known, or at least to discriminate which word cannot be used to be the lacking property. We present results from a set of experiments that provide some insight into aspects of human development.
Proceedings of ICDL- EpiRob 2011: IEEE Conference on Development and Learning, and Epigenetic Robotics , 2011
We present a case-study analysing the prosodic contours and salient word markers of a small corpus of robotdirected speech where the human participants have been asked to talk to the robot as if it were a child. We assess whether such contours and salience characteristics could be used to extract relevant information for the subsequent learning and scaffolding of meaning in robots. The study uses measures of pitch, energy and word duration from the participants speech and exploits Pierrehumbert and Hirschberg's theory of the meaning of intonational contours which may provide information on shared belief between speaker and listener. The results indicate that 1) participants use a high number of contours which provide new information markers to the robot, 2) that prosodic question contours reduce as the interactions proceed and 3) that pitch, energy and duration features can provide strong markers for relevant words and 4) there was little evidence that participants altered their prosodic contours in recognition of shared belief. A description and verification of our software which allows the semi-automatic marking of prosodic phrases is also described.
Spoken language interaction with robots: Recommendations for future research
Computer Speech & Language, 2022
aspects of language, improving robustness, creating new methods for rapid adaptation, better integrating speech and language with other communication modalities, giving speech and language components access to rich representations of the robot's current knowledge and state, making all components operate in real time, and improving research infrastructure and resources. Research and development that prioritizes these topics will, we believe, provide a solid foundation for the creation of speech-capable robots that are easy and effective for humans to work with.