一种用于语音转换的区域最近邻迭代训练算法
doi: 10.3724/SP.J.1146.2012.00398
An Iterative Training Algorithm Based on Local Nearest Neighbor for Voice Conversion
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摘要: 针对非对称语音库情况下的语音转换,该文提出一种新的改进的语音转换训练算法ILNCA。与原有的训练算法INCA不一样的是,ILNCA首先利用高斯混合模型(GMM)分别对源、目标语音特征参数空间进行分类。然后根据KullbackLeibler(KL)距离最小原则对源、目标GMM模型的子空间进行匹配,最后利用最近邻准则在相对应的子空间中进行源、目标语音特征参数矢量的对齐。客观测试和主观听觉实验都表明由于该文算法采用了更加精确的矢量对齐方法,能取得比INCA算法更优异的转换性能。Abstract: A novel algorithm named Iterative combination of a Local nearest Neighbor search step and a Conversion step Alignment (ILNCA), a modified version of the Iterative combination of a nearest Neighor search step and a Conversion step Alignment (INCA), is proposed for training voice conversion system under the situation of nonparallel corpus. Unlike INCA, ILNCA uses firstly Gaussian Mixture Model (GMM) to represent the spectral feature spaces of both source speaker and target speaker respectively, and then matches each individual Gaussian components of the GMM from source speaker to target speaker and vice versa according to Kullback-Leibler (KL) distance. Finally, ILNCA performs the frame alignment of phonetically equivalent acoustic vectors from source and target speaker in their mapped sub-spaces, not in the whole space like INCA. Both object and subject evaluations are conducted. The experimental results demonstrate that the approach can achieve better performance than INCA because of the accurate vector alignment.
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Key words:
- Voice conversion /
- Text independent /
- Nearest neighbor /
- Iterative training
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