计算机工程与科学2018，Vol.40Issue(4)：752-756,5.DOI:10.3969/j.issn.1007-130X.2018.04.024
基于PSO优化GRNN的语音转换方法
Voice conversion based on optimizing GRNN by PSO
摘要
Abstract
The paper proposes a new voice conversion method based on using Particle Swarm Optimization (PSO)to optimize General Regression Neural Network (GRNN).Firstly,the method utilizes the characteristic parameters of the training speaker's vocal tract and source excitation to train two GRNNs,and then obtains the structure parameters of GRNNs.Secondly,in order to reduce the adverse impact of artificial man-induced factors on conversion results,PSO is used to optimize the parameters of the GRNN model.Finally,the pitch contour and the energy profile of prosodic features are linearly converted,thus making the converted voice contain more personalized feature information of source speaker.Experimental results show that,compared with the radial basis function neural network(RBF) and the GRNN based voice conversion methods,our method improves the naturalness and likelihood of the converted voices and evidently decreases the spectral distortion rate,so the converted voices are more closed to the target voices.关键词
语音转换/广义回归神经网络模型/粒子群优化Key words
voice conversion/general regression neural network model/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
王民,杨秀峰,要趁红..基于PSO优化GRNN的语音转换方法[J].计算机工程与科学,2018,40(4):752-756,5.基金项目
住房城乡建设部科学技术项目(2016-R2-045) （2016-R2-045）
西安市碑林区2014年科技计划(GX1412) （GX1412）
