数据采集与处理Issue(2)：336-343,8.DOI:10.16337/j.1004-9037.2015.02.011
基于自适应粒子群优化径向基函数神经网络的语音转换
Voice Conversion Based on Adaptive Particle Swarm Optimization Radial Basis Function Neural Network
摘要
Abstract
Voice conversion is a technique for changing the personality characteristics of a source speaker′s voice into the target speaker′s,while preserving the original semantic information.An adaptive particle swarm optimization (PSO)based method is proposed to model voice features by training the radial basis function (RBF)neural network in order to capture the spectral envelope mapping function between speakers.In addition,the pitch transformation is captured by modeling pitch with the joint spectral fea-ture parameters in RBF neural network,which makes the converted pitch contain more target details.Fi-nally,the performance of the improved voice conversion system is tested by subjective and objective method respectively.Experimental results show that the performance of the proposed method is better than that of the Gaussian mixture model (GMM)based system,especially for the male to female conver-sion.关键词
语音转换/径向基函数神经网络/自适应粒子群优化/高斯混合模型/基频Key words
voice conversion/radial basis function neural network/adaptive particle swarm optimization/gaussian mixture model/pitch分类
信息技术与安全科学引用本文复制引用
张玲华,姚绍芹,解伟超..基于自适应粒子群优化径向基函数神经网络的语音转换[J].数据采集与处理,2015,(2):336-343,8.基金项目
江苏省高校自然科学研究重大(13KJA510003)资助项目 （13KJA510003）
江苏高校优势学科建设工程(PAPD)资助项目 （PAPD）
江苏省普通高校研究生科研创新计划(CXLX12_0478)资助项目。 （CXLX12_0478）
