Speaker diarization systems attempt segmentation and labeling of a conversation between $R$ speakers, while no prior information is given regarding the conversation. Most state of the art diarization systems require the full body of the conversation data prior to the application of some diarization approach. However, for some applications such as forensics, which handles vast amount of data, an on-line or incremental diarization is of high importance. For that purpose, a two-stage incremental diarization of telephone conversations algorithm is suggested. On the first stage, a fully unsupervised diarization algorithm is applied over an initial training segment from the conversation. The second-stage is composed of time-series clustering of increments of the conversation. Applying incremental diarization over 1802 telephone conversations from NIST 2005 SER generated an increase in diarization error of approximately 2% compared to the diarization error of an off-line diarization system