Online split-and-merge EM training of GMM for pattern classification
基于在线分裂合并EM算法的高斯混合模型分类方法*

This paper presented a new online incremental training algorithm of Gaussian mixture model (GMM), which aimed to update GMM model parameters online incrementally instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method was extended on split-and-merge EM procedure by Ueda with a new merge and split operation, so inherently it was also capable to escape from local maxima and reduce the chances of singularities. By introducing the time sequence to all the model parameters, also proposed a new online incremental EM training algorithm to update GMM model parameters sample by sample. Experiments on the synthetic data and speech processing task show the advantages and efficiency of the proposed method.