Hidden CRF Model

A Hidden CRF Model is a CRF Model with Hidden Variables.

• AKA: Hidden Conditional Random Field.
• Context:
  • It is a Discriminative Latent Variable Model.
• See: Hidden Markov Model.

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References

2007

• (Quattoni et al., 2007) ⇒ Ariadna Quattoni, Sybor Wang, Louis-Philippe Morency, Michael Collins, and Trevor Darrell. (2007). “Hidden Conditional Random Fields.” In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(10). doi:10.1109/TPAMI.2007.1124

• (Reiter et al., 2007) ⇒ S. Reiter, B. Schuller, and G. Rigoll. (2007). “Hidden Conditional Random Fields for Meeting Segmentation.” In: Proceedings of Multimedia and Expo Conference. doi:10.1109/ICME.2007.4284731
  • ABSTRACT: Automatic segmentation and classification of recorded meetings provides a basis towards understanding the content of a meeting. It enables effective browsing and querying in a meeting archive. Though robustness of existing approaches is often not reliable enough. We therefore strive to improve on this task by applying conditional random fields augmented by hidden states. These hidden conditional random fields have been proven to be efficient in low level pattern recognition tasks. Now we propose to use these novel models to segment a pre-recorded meeting into meeting events. Since they can also be seen as an extension to hidden Markov models an elaborate comparison of the two approaches is provided. Extensive test runs on the public M4 Scripted Meeting Corpus prove the great performance of applying our suggested novel approach compared to other similar methods.

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