Speaker clustering is a key component in many speech processing applications. This paper addresses the problem of the clustering speakers when no information is available. When performing online speaker clustering

it is common to make clustering decision as soon as an audio segment is received. When the wrong decision is made

the error can propagate the posterior clustering. This paper describes an improved online speaker clustering algorithm which based a decision tree. Unlike typical online clustering approaches

the proposed method constructs a decision tree when an audio segment is received. A pruning strategy for candidate-elimination is also applied. Experiments indicate that the algorithm achieves good performance on both precision and speed. By using the method

average speaker purity is improved by 0.9%

and average cluster purity is improved by 1.1%

and the elapsed-time is reduced by 57%. Experiments also show that this method is effective at improving the performance of the unsupervised adaptation even comparing with the true speaker-condition.