A study of multimodal recognition for human communication search

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We developed multimodal pattern recognition techniques for human communication using speech and video. We proposed a statistical technique using Gaussian mixture models and support vector machines for event extraction. We participated in TRECVID2010 workshop, where our method achieved the 4-th performance among 40 participants from all over the world. We also developed new methods for active learning for speech modeling and adaptation, noise robust speech recognition, signal processing for meeting speech recognition, multimodal pattern recognition, speaker/gesture recognition, speech style analysis and video summarization.