基于GMM和ANN混合模型的语音转换方法

为了克服利用高斯混合模型(GMM)进行语音转换的过程中出现的过平滑现象，考虑到GMM模型参数的均值能够表征转换特征的频谱包络形状，本文提出一种基于GMM与ANN混合模型的语音转换，利用ANN对GMM模型参数的均值进行转换；为了获取连续的转换频谱，采用静态和动态频谱特征相结合来逼近转换频谱序列；鉴于基频对语音转换的重要性，在频谱转换的基础上，对基频也进行了分析和转换。最后，通过主观和客观实验对提出的混合模型的语音转换方法的性能进行测试，实验结果表明，与传统的基于GMM模型的语音转换方法相比，本文提出的方法能够获得更好的转换语音。

Voice conversion based on a mixed model GMM and ANN

In this paper, as the mean vector of GMM parameters can represent the basic shapes of converted feature vectors, a novel mixed model comprised of GMM and ANN spectral conversion method is proposed to alleviate the over-smoothing problem by using ANN to transform the mean vector of GMM parameters. Not only static but also dynamic spectral features are used for approaching converted spectrum sequence in order to gain the continuous converted spectral. Moreover, as pitch is very important to voice conversion, F0 is also analyzed and transformed on the basis of spectral conversion. The performance of the proposed method is evaluated using subjective and objective tests, and the results show that the proposed method can obtain a better speech quality than the earlier voice conversion system based on conventional GMM method.