Improved online speaker clustering based on decision tree

Speaker clustering is a key component in many speech processing applications. To solve the problem of error propagating in the posterior clustering caused by the traditional online clustering, an improved online speaker clustering algorithm based on a decision tree is proposed. Unlike typical online clustering approaches, the proposed method constructs a decision tree to increase branches and to distinguish an audio segment clustering to reduce effectively the effect of error distinguishing on the posterior clustering. To shorten the operation time, a pruning strategy for candidate-elimination is also presented. Experiments indicate that the algorithm achieves good performance on both precision and speed. By using this method, the average speaker purity and the average cluster purity have improved by 0.9% and 1.1% respectively, and the time consuming is reduced by 57%. Experiments also show that this method is effective for improving the performance of the unsupervised adaptation as compared with the true speaker-condition.