关键词: 最近邻识别器, 集成学习, 说话人识别, 分层采样

Abstract: K-Nearest Neighbor is one of the instance-based learning algorithm，it can be very competitive with the state-of-the-art classification methods.Besides simplicity，KNN has better generalization ability and is robust for noisy training data and quite effective when there is sufficiently large set of training data.So it has been widely used in speaker recognition field.Since the generalization ability of an ensemble could be significantly better than that of a single learner，ensemble learning has been a hot topic during the past years.In our paper，we intend to improve the recognition speed and accurate rate by introducing a novel method combining optimizing annular region stratified sampling k nearest neighbor with proposed BagWithProb ensemble learning algorithm.A large empirical study reported in this paper shows that the proposed algorithm can effectively improve the performance of speaker recognition system.

Key words: nearest neighbor learner, ensemble learning, speaker recognition, stratified sampling