Directional-statistics-based mask-based blind speech separation (BSS) clusters normalized time-frequency (TF) observations from M microphones on the complex unit sphere, without relying on plane-wave or spherical-wave assumptions. Existing methods use separately defined angular mixture models, which makes the effect of density shape difficult to isolate. This paper proposes a complex spherical Student's t mixture model (cSTMM) that connects the complex angular central Gaussian mixture model (cACGMM), complex Bingham mixture model (cBMM), and complex Watson mixture model (cWMM) through the degrees of freedom ν and eigenvalue constraints. We derive a latent-scale expectation-maximization (EM) framework with an approximate M-step based on high-concentration approximation (HCA). On noise-free LibriSpeech mixtures reverberated using measured room impulse responses (RIRs), the development-selected value ν*=1 outperformed the cACGMM-equivalent choice ν=M in all 18 test conditions, yielding a mean signal-to-distortion ratio improvement (SDRi) gain of 0.25dB. The model reduces to the cACGMM at ν=M and approaches the cBMM/cWMM in the large-ν limits.