Unsupervised submodular subset selection for speech data
Unsupervised submodular subset selection for speech data
复制标题
语音数据的无监督子模子集选择
DOI：
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发表时间：
2014
期刊：
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通讯作者：
J. Bilmes
中科院分区：
文献类型：
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作者：
K. Wei;Yuzong Liu;K. Kirchhoff;J. Bilmes
我们对选择培训电话识别器的声学数据子集进行了比较研究。将数据选择问题作为一个约束的次体优化问题解决。这种方法的先前应用需要以监督方式训练的转录或声学模型。在本文中，我们开发并评估了一种新颖且完全无监督的方法，并将其应用于Timit数据。结果表明，我们的方法始终优于许多基线方法，同时计算非常有效，并且不需要标记。
We conduct a comparative study on selecting subsets of acoustic data for training phone recognizers. The data selection problem is approached as a constrained submodular optimization problem. Previous applications of this approach required transcriptions or acoustic models trained in a supervised way. In this paper we develop and evaluate a novel and entirely unsupervised approach, and apply it to TIMIT data. Results show that our method consistently outperforms a number of baseline methods while being computationally very efficient and requiring no labeling.