Abstract：To solve the problem that the recognition rate of traditional Gaussian mixture model decreases significantly in noisy conditions, a Gaussian mixture model based on α variable factor-integration is presented by adopting the α-integration mechanism of multiple stochastic models in the form of probability distributions. Through introducing the variable factor, the proportion of different compositions in the mixture model is adjusted again. By re-estimating the proposed model parameters, the experimental results show the performance of the proposed model is better than that of the traditional Gaussian mixture model on databases TIMIT/NTIMIT and different speaker numbers. Especially in noisy conditions with the optimal value of α,the recognition rate is increased by 8%. On NIST evaluation database the experimental results show that the recognition rate is increased as well compared with GMM-UBM system.
李杰，刘贺平. 用于说话人识别的基于可变因子整合的高斯混合模型[J]. 模式识别与人工智能, 2012, 25(6): 937-942.
LI Jie,LIU He-Ping. Gaussian Mixture Model Based on Variable Factor-Integration for Speaker Recognition. , 2012, 25(6): 937-942.
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