||This paper introduces a collaborative personal speaker identification system to annotate conversations and meetings using speech-independent speaker modeling and one audio channel. This system can operate in standalone and collaborative modes, and learn about speakers online that were detected as unknown. In collaborative mode, the system exchanges current speaker information with personal systems of others to improve identification performance. Our collaboration concept is based on distributed personal systems only, hence it does not require a specific infrastructure to operate. We present a generalized description of collaboration situations and derive three use scenarios in which the system was subsequently evaluated. Compared to standalone operation, collaboration among four personal identification systems increased system performance by up to 9% for 4 relevant speakers and up to 21% for 24 relevant speakers. Allowing unknown speakers in a conversation did not impede performance gains of a collaboration. In a scenario where individual systems had nonidentical speaker sets, collaboration gains were 16% for 24 relevant speakers.