Speaker Clustering Based on Non-Negative Matrix Factorization-猫眼课题宝

DOI：

10.21437/interspeech.2011-384

发表时间：

2011

期刊：

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其他

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作者：

M. Nishida;Seiichi Yamamoto

本文解决了多方对话的无监督说话者聚类问题。以往的研究主要采用层次聚类方法。然而，当对话数据中有很多话语时，这些方法需要许多过程，例如距离计算和聚类合并。我们提出了一种基于非负矩阵分解的聚类方法。所提出的方法可以通过分解由模型之间的距离组成的矩阵来执行快速且鲁棒的聚类。我们使用基于贝叶斯信息准则的方法、基于高斯混合模型之间的似然比的方法以及所提出的方法进行了说话人聚类实验。实验结果表明，所提出的方法比这些传统方法获得了更高的聚类精度。索引术语：无监督说话人聚类、非负矩阵分解、凝聚层次聚类、多方对话

This paper addresses unsupervised speaker clustering for multiparty conversations. Hierarchical clustering methods were mainly used in previous studies. However, these methods require many processes, such as distance calculation and cluster merging, when there are many utterances in conversation data. We propose a clustering method based on non-negative matrix factorization. The proposed method can perform fast and robust clustering by decomposing a matrix consisting of distances between models. We conducted speaker clustering experiments using a Bayesian information criterion based method, a method based on the likelihood ratio between Gaussian mixture models, and the proposed method. Experimental results showed that the proposed method achieves higher clustering accuracy than these conventional methods. Index Terms: unsupervised speaker clustering, non-negative matrix factorization, agglomerative hierarchical clustering, multi-party conversation

Speaker Clustering Based on Non-Negative Matrix Factorization

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