NDLI: Semi-supervised meeting event recognition with adapted HMMs

Content Provider	IEEE Xplore Digital Library
Author	Zhang, D. Gatica-Perez, D. Bengio, S.
Copyright Year	2005
Description	Author affiliation: IDIAP Res. Inst., Swiss Fed. Inst. of Technol., Lausanne, Switzerland (Zhang, D.; Gatica-Perez, D.; Bengio, S.)
Abstract	This paper investigates the use of unlabeled data to help labeled data for audio-visual event recognition in meetings. To deal with situations in which it is difficult to collect enough labeled data to capture event characteristics, but collecting a large amount of unlabeled data is easy, we present a semi-supervised framework using HMM adaptation techniques. Instead of directly training one model for each event, we first train a well-estimated general event model for all events using both labeled and unlabeled data, and then adapt the general model to each specific event model using its own labeled data. We illustrate the proposed approach with a set of eight audio-visual events defined in meetings. Experiments and comparison with the fully-supervised baseline method show the validity of the proposed semi-supervised approach.
File Size	239646
File Format	PDF
ISBN	0780393317
DOI	10.1109/ICME.2005.1521618
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-06
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Training data Speech analysis Labeling Surveillance Event detection Testing Ambient intelligence Concatenated codes Character recognition
Content Type	Text
Resource Type	Article

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