This paper discusses the Impact of Voice Trace for Detecting Suspects in a multi-speaker stream for forensic or legal research (IVTDS). This scientific evidence was found in a crime and is exploited for the task of detecting suspects in this audio stream among all involved. So in this framework of the work, the first objective is to obtain segments of homogeneous speakers, that is to say containing the segments of only one speaker. These segments must be as long as possible with a priori information on the number of speakers present in the audio stream and that these speakers do not speak simultaneously. The second objective is to study the impact of scientific evidence on the detection of suspects in this multi-speaker stream assuming that no prior knowledge is available on the suspects. For this, the work takes place in three main phases: the parametrization of the multi-speaker flow, the speaker changes are segmented and detected as valid or otherwise canceled, and finally the voice traces are exploited and compared to the models GMM (Gaussian Mixture Models) and the global model UBM (Universal Background Model) by selecting only the best scores. Applying this approach to data generated from the NIST 2005 telephone-type database. The results of the experiments carried out demonstrate the effectiveness of this approach of detecting suspects using voice traces in solving certain forensic problems.