Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Speaker change detection and tracking in real-time news broadcasting analysis
Proceedings of the tenth ACM international conference on Multimedia
Wearable sensing to annotate meeting recordings
Personal and Ubiquitous Computing
Sensing and modeling human networks
Sensing and modeling human networks
MyLifeBits: a personal database for everything
Communications of the ACM - Personal information management
InSense: Interest-Based Life Logging
IEEE MultiMedia
Predicting shoppers' interest from social interactions using sociometric sensors
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Audiovisual Probabilistic Tracking of Multiple Speakers in Meetings
IEEE Transactions on Audio, Speech, and Language Processing
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This paper introduces a collaborative personal speaker identification system to annotate conversations and meetings using speech-independent speaker modeling and one audio channel. This system can operate in standalone and collaborative modes, and learn about speakers online that were detected as unknown. In collaborative mode, the system exchanges current speaker information with personal systems of others to improve identification performance. Our collaboration concept is based on distributed personal systems only, hence it does not require a specific infrastructure to operate. We present a generalized description of collaboration situations and derive three use scenarios in which the system was subsequently evaluated. Compared to standalone operation, collaboration among four personal identification systems increased system performance by up to 9% for 4 relevant speakers and up to 21% for 24 relevant speakers. Allowing unknown speakers in a conversation did not impede performance gains of a collaboration. In a scenario where individual systems had nonidentical speaker sets, collaboration gains were 16% for 24 relevant speakers.