In this study, we propose a speaker clustering algorithm based on reading and singing speech samples for each speaker. As a speaking style, singing introduces changes in the time-frequency structure of a speaker's voice. The purpose of this study is to introduce advancements into speech systems such as speech indexing and retrieval which improve robustness to intrinsic variations in speech production. Clustering is performed within a GMM mean supervector space. The proposed method includes two stages. First, initial clusters are obtained using traditional clustering techniques such as k-means, and hierarchical. Next, each cluster is refined in a PLDA subspace resulting in a more speaker dependent representation that is less sensitive to speaking style. The proposed algorithm improves the average clustering accuracy of the k-means baseline by +9.3% absolute.
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