Online speaker clustering using incremental learning of an ergodic hidden Markov model

Authors:
Takafumi Koshinaka;Kentaro Nagatomo;Koichi Shinoda
Affiliations:
Common Platform Software Res. Labs., NEC Corporation, Kawasaki, Japan;Common Platform Software Res. Labs., NEC Corporation, Kawasaki, Japan;Dept. of Computer Science, Tokyo Institute of Technology, Japan
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

A novel online speaker clustering method suitable for real-time applications is proposed. Using an ergodic hidden Markov model, it employs incremental learning based on a variational Bayesian framework and provides probabilistic (non-deterministic) decisions for each input utterance, directly considering the specific history of preceding utterances. It makes possible more robust cluster estimation and precise classification of utterances than do conventional online methods. Experiments on meeting-speech data show that the proposed method produces 70–80% fewer errors than a conventional method does.