+ U% u) \7 E: b$ `! I- H! @6 xExpectation-Maximization (EM) algorithm is usually used to estimate parameters of Gaussian mixture model. Due to the hill-climbing characteristic of EM, any arbitrary estimation of the initial model parameters will usually lead to a sub-optimal model in practice. To resolve this problem, a hybrid training method based on Particle Swarm Optimi- zation (PSO) is proposed. It utilizes the global searching capability of PSO and combines the effectiveness of EM. The particles perform basic operations of PSO (velocity updating and position updating) and EM algorithm, which can ex- plore the training speech space to move toward the global optimum. The dependence of the final model parameters on the selection of the initial model parameters is also reduced. Experimental results have showed that this method can obtain more optimized GMM parameters and has better capability than EM in speaker recognition. 1 C O( a- W; M( T3 r1 m$ o5 S8 Q: @; X6 ~1 |! L& X/ o- C {9 D
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原文出处:汉斯期刊《软件工程与应用》 ' D6 v2 _7 Y J. ~5 l说话人识别中基于粒子群优化的GMM训练方法 J- Y6 n) I1 W& n2 y6 `Gaussian Mixture Model Training Method Based on Particle Swarm Optimizer for Speaker Recognition+ K2 B7 r5 @ {7 d
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