Speaker diarization is usually performed in a blind manner without using a priori knowledge about the identity or acoustic characteristics of the participating speakers. In this paper we propose a novel framework for incorporating available a priori knowledge such as potential participating speakers, channels, background noise and gender, and integrating these knowledge sources into blind speaker diarization-type algorithms. We demonstrate this framework on two tasks. The first task is agent-customer speaker diarization for call-center phone calls and the second task is speaker-diarization for a PDA recorder which is part of an assistive living system for the elderly. For both of these tasks, incorporating the a priori information into our blind speaker diarization systems significantly improves diarization accuracy. Copyright © 2011 ISCA.