Abstract

Unsupervised speaker indexing sequentially detects points where a speaker identity changes in a multispeaker audio stream, and categorizes each speaker segment, without any prior knowledge about the speakers. This paper 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. Once a speaker-independent model is selected from the generic sample models, it is progressively adapted into a specific speaker-dependent model. Experiments were performed with data from the Speaker Recognition Benchmark …

Date

2005

Authors

Soonil Kwon, Shri Narayanan

Journal

IEEE transactions on speech and audio processing

Volume

13

Issue

5

Pages

1004-1013

Publisher

IEEE