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Speaker Diarization is the process of partitioning an audio input into homogeneous segments according to speaker identity where the number of speakers in a given audio input is not known a priori. This master thesis presents a novel initialization method for Speaker Diarization that requires less manual parameter tuning than most current GMM/HMM based agglomerative clustering techniques and is more accurate at the same time. The thesis reports on empirical research to estimate the importance of each of the parameters of an agglomerative-hierarchical-clustering-based Speaker Diarization system and evaluates methods to estimate these parameters completely unsupervised. The parameter estimation combined with a novel non-uniform initialization method result in a system that performs better than the current ICSI baseline engine on datasets of the National Institute of Standards and Technology (NIST) Rich Transcription evaluations of the years 2006 and 2007 (17% overall relative improvement).