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

摘要

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.