Broadcast News Story Segmentation Using Conditional Random Fields and Multimodal Features

  • WANG Xiaoxuan
    School of Computer Science, Northwestern Polytechnical University
  • XIE Lei
    School of Computer Science, Northwestern Polytechnical University
  • LU Mimi
    School of Computer Science, Northwestern Polytechnical University
  • MA Bin
    Institute for Infocomm Research
  • CHNG Eng Siong
    School of Computer Engineering, Nanyang Technological University
  • LI Haizhou
    Institute for Infocomm Research

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  • セレクト及びマージ頂点数の最小化によるパイプライン化依存性グラフの簡単化
  • セレクト オヨビ マージ チョウテンスウ ノ サイショウカ ニ ヨル パイプラインカ イソンセイ グラフ ノ カンタンカ

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In this paper, we propose integration of multimodal features using conditional random fields (CRFs) for the segmentation of broadcast news stories. We study story boundary cues from lexical, audio and video modalities, where lexical features consist of lexical similarity, chain strength and overall cohesiveness; acoustic features involve pause duration, pitch, speaker change and audio event type; and visual features contain shot boundaries, anchor faces and news title captions. These features are extracted in a sequence of boundary candidate positions in the broadcast news. A linear-chain CRF is used to detect each candidate as boundary/non-boundary tags based on the multimodal features. Important interlabel relations and contextual feature information are effectively captured by the sequential learning framework of CRFs. Story segmentation experiments show that the CRF approach outperforms other popular classifiers, including decision trees (DTs), Bayesian networks (BNs), naive Bayesian classifiers (NBs), multilayer perception (MLP), support vector machines (SVMs) and maximum entropy (ME) classifiers.



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