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Automatic Segmentation, Classification and Clustering of Broadcast News Audio

机译：广播新闻音频的自动分段，分类和聚类

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Automatic recognition of broadcast feeds from radio and television sources has been gaining importance recently, especially with the success of systems such as the CMU Informedia system [1]. In this work we describe the problems faced in adapting a system built to recognize one utterance at a time to a task that requires recognition of an entire half hour show. We break the problem into three components: segmentation, classification, and clustering. We show that a priori knowledge of acoustic conditions and speakers in the broadcast data is not required for segmentation. The system is able to detect changes in acoustics, recognize previously observed conditions, and use this to pool adaptation data. We also describe a novel application of the Symmetric Kullback-Leibler distance metric that is used as a single solution to both the segmentation and clustering problems. The three components are evaluated through comparisons between the Partitioned and Unpartitioned components of the 1996 ARPA Hub 4 evaluation test set.
机译：最近，自动识别来自广播和电视源的广播提要变得越来越重要，尤其是随着诸如CMU Informedia系统[1]之类的系统的成功。在这项工作中，我们描述了将一个系统识别为一次识别一种话语的系统适应需要识别整个半小时节目的任务所面临的问题。我们将问题分为三个部分：细分，分类和聚类。我们表明，分割不需要广播数据中的声学条件和扬声器的先验知识。该系统能够检测到声学变化，识别先前观察到的状况，并以此来收集适应数据。我们还描述了对称Kullback-Leibler距离度量的一种新颖应用，该度量被用作分割和聚类问题的单个解决方案。通过比较1996 ARPA Hub 4评估测试集中的分区和未分区组件，对这三个组件进行了评估。

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