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  1. Proceedings of the 2014 Workshop on Multimodal, Multi-Party, Real-World Human-Robot Interaction (MMRWHRI '14)
  2. Selection of an Object Requested by Speech Based on Generic Object Recognition
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Towards Closed Feedback Loops in HRI: Integrating InproTK and PaMini
Advances in Wikipedia-based Interaction with Robots
Self-calibration of an Assistive Device to Adapt to Different Users and Environments
Attention Detection in Elderly People-Robot Spoken Interaction
Towards proactive robot behavior based on incremental language analysis
Selection of an Object Requested by Speech Based on Generic Object Recognition
Clarification Dialogues for Perception-based Errors in Situated Human-Computer Dialogues
Applying Topic Recognition to Spoken Language in Human-Robot Interaction Dialogues
Applying Semantic Web Services to Multi-Robot Coordination
Affective Feedback for a Virtual Robot in a Real-World Treasure Hunt

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Selection of an Object Requested by Speech Based on Generic Object Recognition

Content Provider ACM Digital Library
Author Nishimura, Hitoshi Ozasa, Yuko Nakano, Mikio Ariki, Yasuo
Abstract In this paper, we propose a method that a robot can select an object specified by human speech among several objects based on generic object recognition. Although object selection methods have been proposed based on specific object recognition, generic object recognition is more useful for the selection in a real environment. In the proposed method, an object is selected by integrating speech recognition results and generic object recognition results. We investigated the relation between the method of narrowing down candidates based on speech and image recognition results and the object selection accuracy.
Starting Page 23
Ending Page 24
Page Count 2
File Format PDF
ISBN 9781450305518
DOI 10.1145/2666499.2666505
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-16
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Multimodality Speech recognition Generic object recognition
Content Type Text
Resource Type Article
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