短时频谱通用背景模型群联合韵律的年龄语音转换

提出一种短时频谱通用背景模型群与韵律参数相结合进行年龄语音转换的方法。谱参数转换方面,同一年龄段各说话者提取语音短时谱系数并建立高斯混合模型,然后依据语音特征相似性对说话者进行聚类,每一类训练一个通用背景模型,最终得到通用背景模型群和一组短时频谱转换函数。谱参数转换之后再对共振峰进一步微调。韵律参数转换方面,基频和语速分别建立单高斯和平均时长率模型来推导转换函数。实验结果显示,提出的方法在ABX和MOS等评价指标上比传统的双线性法有明显的优势,相对单一通用背景模型法的对数似然度变化率提高了4%。这一结果表明提出的方法能够使转换语音具有良好目标倾向性的同时有较好的语音质量,性能较传统方法有明显提升。 

Voice conversion of different ages using universal background model groups of short-time spectra and prosodic features

For the voice conversion of different ages, a method using Universal Background Model Groups(UBMG) of short-time spectra and prosodic features is proposed. In spectrum aspect, Gaussian Mixture Model(GMM) is trained for every speaker after extracting linear predictive cepstrum coefficients, then the speakers in the same age period are clustered based on their voice similarity, and each cluster is further trained to be a UBM of spectrum distribution.Finally, an UBM group and corresponding spectrum conversion functions are obtained in each age period. Formants adjustment is further used after spectrum conversion. Furthermore, fundamental frequency and speech rate are modeled by single Gaussian and average duration rate respectively to derive their conversion functions in the aspect of prosodic features. The results of objective and subjective evaluation experiments such as ABX and MOS show that the proposed method has a distinct advantage compared with conventional bilinear method and its change rate of log-likelihood ratio increases by 4% compared with single UBM method. The results show the proposed method can make the converted speech more close to the speech of target age period with good speech quality while the performance has been improved evidently compared with conventional methods.