Abstract

The paper presents the ELISA consortium activities in automatic speaker segmentation, also known as speaker diarization, during the NIST rich transcription (RT), 2003, evaluation. The experiments were conducted on real broadcast news data (HUB4). Two different approaches from the CLIPS and LIA laboratories are presented and different possibilities of combining them are investigated, in the framework of the ELISA consortium. The system submitted as an ELISA primary system obtained the second lowest segmentation error rate compared to the other RT03-participant primary systems. Another ELISA system submitted as a secondary system outperformed the best primary system and obtained the lowest speaker segmentation error rate.

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