On-line Speaker Indexing Project Role in Support of Imsc Strategic Plan Discussion of Methodology Used

Unsupervised speaker indexing sequentially detects points where a speaker identity changes in a multi-speaker audio stream, and categorizes each speaker segment, without any prior knowledge about the speakers. This project addresses two challenges: The first relates to sequential speaker change detection. The second relates to speaker modeling in light of the fact that the number/identity of the speakers is unknown. To address this issue, a predetermined generic speaker-independent model set, called the Sample Speaker Models (SSM), is proposed. This set can be useful for more accurate speaker modeling and clustering without requiring training models on target speaker data.