Hidden Conditional Random Fields for Meeting Segmentation
Hidden Conditional Random Fields for Meeting Segmentation
复制标题
用于会议分段的隐藏条件随机字段
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
G. Rigoll
中科院分区：
文献类型：
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作者：
S. Reiter;Björn Schuller;G. Rigoll
记录会议的自动分段和分类为理解会议内容提供了基础。它可以在会议存档中进行有效的浏览和查询。尽管现有方法的鲁棒性往往不够可靠。因此，我们努力通过应用由隐藏状态增强的条件随机场来改进这项任务。这些隐藏的条件随机场已被证明是有效的低水平的模式识别任务。现在，我们建议使用这些新的模型分割预先录制的会议到会议事件。由于它们也可以被看作是隐马尔可夫模型的扩展，提供了两种方法的详细比较。在公开的M4脚本会议语料库上进行了广泛的测试，证明了与其他类似方法相比，应用我们提出的新方法的出色性能。
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.