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AUTOMATIC SPEAKER CLUSTERING

机译：自动扬声器集群

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This paper presents a fully automatic speaker clustering algorithm, which consists of three components: building a distance matrix based on Gaussian models of the acoustic segments; performing hierarchical clustering on the distance matrix with the prior assumption that consecutive segments should be more likely to come from the same speaker; and selecting the best clustering solution automatically by minimizing the within-cluster dispersion with some penalty against too many clusters. We applied this automatic speaker clustering technique in 1996 Hub4 evaluation, and the results show that it contributed significantly to the word error rate (WER) reduction in unsupervised adaptation. From our experiments, the algorithm seldom misclassifies segments from the same speaker into different clusters. We used the same clustering procedure for both partitioned evaluation (PE) and unpartitioned evaluation (UE) tests [1]. Experiments also show that this automatic speaker clustering algorithm improves unsupervised adaptation as much as the hand labeled ideal case where the clusters are generated based on true speaker, channel and background condition.
机译：本文提出了一种全自动的说话人聚类算法，它由三个部分组成：基于声学片段的高斯模型建立距离矩阵；在事先假设连续片段应该更可能来自同一说话者的前提下，对距离矩阵进行分层聚类；并通过最大程度地降低集群内部分散度（对过多集群造成一些损失）来自动选择最佳集群解决方案。我们在1996年Hub4评估中应用了这种自动的说话人聚类技术，结果表明，它在无监督适应中对降低词错误率（WER）起到了重要作用。从我们的实验来看，该算法很少将来自同一说话者的片段分类为不同的簇。我们对分区评估（PE）和未分区评估（UE）测试使用了相同的聚类过程[1]。实验还表明，这种自动的说话人聚类算法可以改善无监督的适应性，就像手工标记的理想情况一样，后者是根据真实的说话人，声道和背景条件生成聚类的。

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