Fast speaker diarization based on binary keys

Splitting a speech signal into speakers is the main goal of a speaker diarization system, which has become an important building block in many speech processing algorithms. Current state of the art systems are able to obtain good diarization error rates, but most of them are rather slow, which is a strong handicap in applications that require overall faster than real-time processing. In this paper we present a novel speaker diarization system which is built following a bottom-up agglomerative clustering approach and based on speaker binary keys, recently proposed for speaker modeling. After initialization, processing is entirely done over binary vectors and using exclusively binary metrics, which makes the system very fast. On tests performed using all conference meetings datasets released for the NIST RT evaluation campaigns we achieve diarization error rates just slightly worse than a classic acoustic-based system while running over 10 times faster.