Persistent Lexical Entrainment in HRI
Jürgen Brandstetter, Eduardo Sandoval, Clay Beckner, Christoph Bartneck
In this study, we set out to ask three questions. First, does lexical entrainment with a robot interlocutor persist after an interaction? Second, how does the influence of social robots on humans compare with the influence of humans on each other? Finally, what role is played by personality traits in lexical entrainment to robots, and how does this compare with the role of personality in entrainment to other humans? Our experiment shows that first, robots can indeed prompt lexical entrainment that persists after an interaction is over. This finding is interesting since it demonstrates that speakers can be linguistically influenced by a robot, in a way that is not merely motivated by a desire to be understood. Second, we find similarities between lexical entrainment to the robot peer and lexical entrainment to a human peer, although the effects are stronger when the peer is human. Third, we find that whether the peer is a robot or a human, similar personality traits contribute to lexical entrainment. In both peer conditions, participants who score higher on “Openness to experience” are more likely to adopt less likely terminology.
Conversational Bootstrapping and Other Tricks of a Concierge Robot
Shang Guo, Jonathan Lenchner, Jonathan Connell, Mishal Dholakia, Hidemasa Muta
We describe the effective use of online learning to enhance the conversational capabilities of a greeter robot that we have been developing over the last two years. The robot was designed to interact naturally with visitors and uses a speech recognition system in conjunction with a natural language classifier. The online learning component monitors interactions and collects explicit and implicit user feedback from a conversation and feeds it back to the classifier in the form of new class instances and adjusted threshold values for triggering the classes. In addition, it enables a trusted master to teach it new question-answer pairs via question-answer paraphrasing, and solicits help with maintaining question-answer-class relationships when needed, obviating the need for explicit programming. The system has been completely implemented and demonstrated using the SoftBank Robotics humanoid robots Pepper and NAO, and the telepresence robot known as Double from Double Robotics.
Child Speech Recognition in Human-Robot Interaction: Evaluations and Recommendations
James Kennedy, Severin Lemaignan, Caroline Montassier, Pauline Lavalade, Bahar Irfan, Fotios Papadopoulos, Emmanuel Senft, Tony Belpaeme
An increasing number of human-robot interaction (HRI) studies are now taking place in applied settings with children. These interactions often hinge on verbal interaction to effectively achieve their goals. Great advances have been made in adult speech recognition and it is often assumed that these advances will carry over to the HRI domain and to interactions with children. In this paper, we evaluate a number of automatic speech recognition (ASR) engines under a variety of conditions, inspired by real-world social HRI conditions. Using the data collected we demonstrate that there is still much work to be done in ASR for child speech, with interactions relying solely on this modality still out of reach. However, we also make recommendations for child-robot interaction design in order to maximise the capability that does currently exist.
Creating Prosodic Synchrony for a Robot Co-player in a Speech-controlled Game for Children
Najmeh Sadoughi, André Pereira, Rishub Jain, Iolanda Leite, Jill Lehman
Synchrony is an essential aspect of human-human interactions. In previous work, we have seen how synchrony manifests in low-level acoustic phenomena like fundamental frequency, loudness, and the duration of keywords during the play of child-child pairs in a fast-paced, cooperative, language-based game. The correlation between the increase in such low-level synchrony and increase in enjoyment of the game suggests that a similar dynamic between child and robot co-players might also improve the child’s experience. We report an approach to creating on-line acoustic synchrony by using a dynamic Bayesian network learned from prior recordings of child-child play to select from a predefined space of robot speech in response to real-time measurement of the child’s prosodic features. Data were collected from 40 new children, each playing the game with both a synchronizing and non-synchronizing version of the robot. Results show a significant order effect: although all children grew to enjoy the game more over time, those that began with the synchronous robot maintained their own synchrony to it and achieved higher engagement compared with those that did not.
Telling Stories to Robots: The Effect of Backchanneling On A Child’s Storytelling
Hae Won Park, Mirko Gelsomini, Jin Joo Lee, Cynthia Breazeal
We developed a nonverbal backchanneling model to improve the ability for a social robot to interact with a child as an attentive listener. We provide an extensive analysis of young children’s nonverbal behavior with respect to how they encode and decode listener responses and speaker cues. Through a data collection of child dyads in peer-to-peer storytelling interactions, we identify attentive listener behaviors as well as speaker cues that prompt opportunities for listener backchannels. Based on our findings, we developed a backchannel opportunity prediction (BOP) model that detects four main speaker cue events based on prosodic features in speech. The rule-based model is capable of accurately predicting backchanneling opportunities in our corpora. We evaluate this model in a human-subjects study where children told stories to an audience of two robots, each with a different backchanneling strategy. We find that our BOP model produces contingent backchanneling responses that convey more attentive listening behavior, and children prefer telling stories to the BOP model robot.
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation
Andrea Daniele, Mohit Bansal, Matthew Walter
Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a navigational guide model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then “translate” this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% when compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants that demonstrate that our method generates instructions that people follow as accurately and easily as those produced by humans.
Event Timeslots (1)
Tue, Mar 7
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Human-Robot Dialog