Speaker diarization using data-driven audio sequencing

In this paper, a speaker diarization system based on data-driven segmentation is proposed. In addition to the usual segmentation and clustering steps, a new module which detects repeated segments between the same shows broadcasted on different dates is added. This process is achieved by using the ALISP-based audio identification system which segments audio data into pseudo-phonetic units. The ALISP segmentation is then used to identify the similar audio segments in TV and radio shows. The system was evaluated during the ETAPE 2011 evaluation campaign and obtained a Diarization Error Rate - DER of 16.23% which was the best result among seven participants.