This research aims at designing an autonomous speaker diarization system able to adapt and evaluate itself. The goal of this research was to apply the concept of human-assisted lifelong learning to the speaker diarization task, also known as speaker segmentation and clustering. More specifically, this work aims at designing an efficientway of interaction between the automatic diarization system and a human domain expert to improve the quality ofdiarization generated by an automatic system while limiting the workload for the human domain expert. This manuscriptproposes an alternative point of view on the definition of the lifelong learning intelligent systems, a dataset designed for evaluation of the lifelong learning diarization systems and the metric for evaluation of human-assisted systems.The main contribution of this work lies in the development of the human-assisted within-show and cross-show diarization methods.