This paper presents different pre-processing techniques, coupled with three speaker diarization systems in the framework of the NIST 2005 Spring Rich Transcription campaign (RT'05S). The pre-processing techniques aim at providing a signal quality index in order to build unique " virtual " signal obtained from all the microphone recordings available for a meeting. The unique " virtual " signal relies on a weighted sum of the different microphones while the signal quality index is given according to a signal to noise ratio. Two methods are used in this paper to compute the instantaneous signal to noise ratio: speech activity detection based approach and a noise spectrum estimate. The speaker diarization task is performed using systems developed by different labs: the LIA, LIUM and CLIPS. Among the different system submissions made by these three labs, the best system obtained 24.5 % speaker diarization error for the conference subdomain and 18.4 % for lecture subdomain.