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In this paper, we present an application of student's t-test to measure the similarity between two speaker models. The measure is evaluated by comparing with other distance metrics: the Generalized Likelihood Ratio, the Cross Likelihood Ratio and the Normalized Cross Likelihood Ratio in speaker detection task. We also propose an objective criterion for speaker clustering. The criterion deduces the number of speakers automatically by maximizing the separation between intra-speaker distances and inter-speaker distances. It requires no development data and works well with various distance metrics. We then report the performance of our proposed similarity distance measure and objective criterion in speaker diarization task. The system produces competitive results: low speaker diarization error rate and high accuracy in detecting number of speakers.