For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competencies and integrating them to form an autonomous robotic system for evaluation "in the wild." The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions.
Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI.
The aim of the present paper is to describe, in acoustic and perceptual terms, the prosodic pattern distinguishing English compound and non-compound noun phrases, and to determine how information structure and position affect the production and perception of the two forms. The study is based on the performance of ten English-speaking subjects (five speakers and five listeners). The test utterances were three minimal-pair noun phrases of two constituents, excised from conversational readings. These were analyzed acoustically, and submitted to the listeners for semantic identification. The results indicate that the distinction, when effective, lies primarily in the different prominence pattern: a sequence of an accented constituent followed by an unaccented one in compounds, and of two accented constituents (the second heard as stronger than the first) in non-compounds. It is also based on a different degree of internal cohesion, stronger in compounds and weaker in non-compounds. F0, associated or trading with intensity, has proved to be the main cue to this distinction--more than duration, the major differentiating parameter in production. When an item is excised from the context, the perception of the intended category depends heavily on the communicative importance it had in the discourse. This means that information structure, through its effects on accentuation, becomes the determining factor in the perception of the distinction. The distinctive accentual pattern weakens or is completely neutralized when the test items convey old information. The degree of deaccentuation also seems to be affected by an immediately following focus, and, to a certain extent, by position. The data are viewed in the framework of speaker-listener interaction, and it is argued that deaccentuation, as well as accentuation, can have a communicative function.
A timbre classification system based on auditory processing and Kohonen self organizing neural networks is described. Preliminary results are given on a simple classification experiment involving 12 instruments in both clean and degraded conditions.
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