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183
VoiceLabel: Using Speech to Label . . .
, 2008
"... Many mobile machine learning applications require collecting and labeling data, and a traditional GUI on a mobile device may not be an appropriate or viable method for this task. This paper presents an alternative approach to mobile labeling of sensor data called VoiceLabel. VoiceLabel consists of t ..."
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interface for reviewing and correcting data labels using a combination of the audio stream, the system’s analysis of that audio, and the corresponding mobile sensor data. A study with ten participants showed that VoiceLabel is a viable method for labeling mobile sensor data. VoiceLabel also illustrates
Learning words from sights and sounds: a computational model
, 2002
"... This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross-modal structure. The model has been imple ..."
Abstract
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Cited by 270 (31 self)
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. These results demonstrate the possibility of using state-of-the-art techniques from sensory pattern recognition and machine learning to implement cognitive models which can process raw sensor data without the need for human transcription or labeling.
DUB Group
"... Many mobile machine learning applications require collecting and labeling data, and a traditional GUI on a mobile device may not be an appropriate or viable method for this task. This paper presents an alternative approach to mobile labeling of sensor data called VoiceLabel. VoiceLabel consists of t ..."
Abstract
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interface for reviewing and correcting data labels using a combination of the audio stream, the system’s analysis of that audio, and the corresponding mobile sensor data. A study with ten participants showed that VoiceLabel is a viable method for labeling mobile sensor data. VoiceLabel also illustrates
Supervised semantic labeling of places using information extracted from sensor data
- Robotics and Autonomous Systems
, 2007
"... Abstract — Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating t ..."
Abstract
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Cited by 50 (8 self)
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Abstract — Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating
Activity recognition using cell phone accelerometers
- Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data
, 2010
"... Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, directio ..."
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Cited by 131 (8 self)
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, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses
Autonomous planned color learning on a mobile robot without labeled data
- in The Ninth IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV
, 2006
"... Color segmentation is a challenging yet integral subtask of mobile robot systems that use visual sensors, especially since they typically have limited computational and memory resources. We present an online approach for a mobile robot to autonomously learn the colors in its environment without any ..."
Abstract
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Cited by 9 (7 self)
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Color segmentation is a challenging yet integral subtask of mobile robot systems that use visual sensors, especially since they typically have limited computational and memory resources. We present an online approach for a mobile robot to autonomously learn the colors in its environment without any
A Self-Labeling Speech Corpus: Collecting Spoken Words with an Online Educational Game
"... We explore a new approach to collecting and transcribing speech data by using online educational games. One such game, Voice Race, elicited over 55,000 utterances over a 22 day period, representing 18.7 hours of speech. Voice Race was designed such that the transcripts for a significant subset of ut ..."
Abstract
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Cited by 14 (3 self)
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We explore a new approach to collecting and transcribing speech data by using online educational games. One such game, Voice Race, elicited over 55,000 utterances over a 22 day period, representing 18.7 hours of speech. Voice Race was designed such that the transcripts for a significant subset
An Entity Maintenance and Connection Service for Sensor Networks
- In The First Intl. Conference on Mobile Systems, Applications, and Services (MobiSys
, 2003
"... In this paper, we present a middleware architecture for coordination services in sensor networks that facilitates interaction between groups of sensors which monitor different environmental events. It sits on top of the native routing infrastructure and exports the abstraction of mobile communicatio ..."
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Cited by 51 (14 self)
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communication endpoints maintained at the locations of such events. A single logical destination is created and maintained for every environmental event of interest. Such destinations are uniquely labeled and can be used for communication by application-level algorithms for coordination and sensory data
Community-Guided Learning: Exploiting Mobile Sensor Users to Model Human Behavior
"... Modeling human behavior requires vast quantities of accurately labeled training data, but for ubiquitous people-aware applications such data is rarely attainable. Even researchers make mistakes when labeling data, and consistent, reliable labels from low-commitment users are rare. In particular, use ..."
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Cited by 11 (5 self)
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and the unconstrained labels to intelligently group crowd-sourced data. We demonstrate how to use similarity measures to determine when and how to split and merge contributions from different labeled categories and present experimental results that demonstrate the effectiveness of our framework.
Recognizing Activities from Mobile Sensor Data: Challenges and Opportunities
"... Mobile sensing data is frequently used to infer a user’s current activity. We argue that while past research on activity recognition has had some notable successes, there are a number of challenges that the community needs to tackle. In particular, establishing an agreed on set of activities would h ..."
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Cited by 1 (0 self)
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Mobile sensing data is frequently used to infer a user’s current activity. We argue that while past research on activity recognition has had some notable successes, there are a number of challenges that the community needs to tackle. In particular, establishing an agreed on set of activities would
Results 1 - 10
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183