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Acoustics, Speech, and Signal Processing, IEEE International Conference on (2009)
Taipei, Taiwan
Apr. 19, 2009 to Apr. 24, 2009
ISBN: 978-1-4244-2353-8
pp: 3721-3724
Gokhan Tur , SRI International, Speech Technology and Research Lab, Menlo Park, CA, 94025, USA
Inspired by popular co-training and domain adaptation methods, we propose a co-adaptation algorithm. The goal is improving the performance of a dialog act segmentation model by exploiting the vast amount of unlabeled data. This task provides a nice framework for multiview learning, as it has been shown that lexical and prosodic features provide complementary information. Instead of simply adding machine-labeled data to the set of manually labeled data, co-adaptation technique adapts the existing models. While both co-training and domain adaptation techniques have been employed for dialog act segmentation, our experiments show that the proposed co-adaptation algorithm results in significantly better performance.

G. Tur, "Co-adaptation: Adaptive co-training for semi-supervised learning," Acoustics, Speech, and Signal Processing, IEEE International Conference on(ICASSP), Taipei, Taiwan, 2009, pp. 3721-3724.
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