Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enabling effective human-robot interaction using perspective-taking in robots
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Integrating vision and audition within a cognitive architecture to track conversations
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
Discovery of sound sources by an autonomous mobile robot
Autonomous Robots
Using reinforcement learning to create communication channel management strategies for diverse users
SLPAT '10 Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies
Towards a formalization of social spaces for socially aware robots
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
Evaluating the utility of auditory perspective-taking in robot speech presentations
CMMR/ICAD'09 Proceedings of the 6th international conference on Auditory Display
Human-aware robot navigation: A survey
Robotics and Autonomous Systems
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Effective communication with a mobile robot using speech is a difficult problem even when you can control the auditory scene. Robot ego-noise, echoes, and human interference are all common sources of decreased intelligibility. In real-world environments, however, these common problems are supplemented with many different types of background noise sources. For instance, military scenarios might be punctuated by high decibel plane noise and bursts from weaponry that mask parts of the speech output from the robot. Even in non-military settings, however, fans, computers, alarms, and transportation noise can cause enough interference that they might render a traditional speech interface unintelligible. In this work, we seek to overcome these problems by applying robotic advantages of sensing and mobility to a text-to-speech interface. Using perspective taking skills to predict how the human user is being affected by new sound sources, a robot can adjust its speaking patterns and/or reposition itself within the environment to limit the negative impact on intelligibility, making a speech interface easier to use.