自动化学报Issue(6)：1198-1207,10.DOI:10.3724/SP.J.1004.2014.01198
基于双因子高斯过程动态模型的声道谱转换方法
Vocal Tract Spectrum Conversion Using a Two-factor Gaussian Process Dynamic Model
摘要
Abstract
We developed in a previous work a two-factor Gaussian process latent variable model (TF-GPLVM) to perform spectral conversion using a strategy of speaker characteristics replacement. Despite its improved performance compared with traditional mapping-based methods, the model suffers from two drawbacks: 1) it cannot capture the speech dynamical characteristics, and 2) there is a large number of parameters to estimate. To overcome these two drawbacks, we propose in this paper to combine TF-GPLVM with hidden Markov model (HMM), and develop an enhanced two-factor Gaussian process dynamic model (TF-GPDM). In the model, the speech dynamics are modeled by state transition probability of HMM, meanwhile speech frames are categorized into a limited number of phonetic content classes using HMM states. Both subjective and objective evaluations show that, compared with both traditional mapping-based methods, such as Gaussian mixture model (GMM) and FW, and TF-GPLVM based one, the proposed TF-GPDM not only improves the speech quality and identity similarity, but also reaches a better compromise between the two dimensions.关键词
声道谱转换/高斯过程隐变量模型/双因子模型/隐马尔科夫模型/语音动态特征Key words
Vocal tract spectrum conversion/Gaussian process latent variable model (GPLVM)/two-factor model/hidden Markov model (HMM)/speech dynamical characteristics引用本文复制引用
孙新建,张雄伟,杨吉斌,曹铁勇,钟新毅..基于双因子高斯过程动态模型的声道谱转换方法[J].自动化学报,2014,(6):1198-1207,10.基金项目
国家自然科学基金(61072042),江苏省自然科学基金(BK2012510),解放军理工大学预先研究基金(20110205,20110211)资助@@@@Supported by National Natural Science Foundation of China (61072042), Natural Science Foundation of Jiangsu Province (BK2012510), and Pre-research Foundation of PLA University of Science and Technology (20110205,20110211) （61072042）
