EML Submission to Albayzin 2018 Speaker Diarization Challenge

Speaker diarization, who is speaking when, is one of the most challenging tasks in speaker recognition, as usually no prior information is available about the identity and the number of the speakers in an audio recording. The task will be more challenging when there is some noise or music on the background and the speakers are changed more frequently. This usually hap-pens in broadcast news conversations. In this paper, we use the EML speaker diarization system as a participation to the recent Albayzin Evaluation challenge. The EML system uses a real-time robust algorithm to make decision about the identity of the speakers approximately every 2 sec. The experimental results on about 16 hours of the developing data provided in the challenge show a reasonable accuracy of the system with a very low computational cost.