短语音说话人识别新方法的研究

Novel Speaker Recognition Method Based on Little Speech Data

提出了一种较短训练语音的说话人识别新方法。利用模糊核聚类算法设计矢量量化器,对说话人的语音特征进行训练。模糊核矢量量化器将原始空间通过非线性映射到高维特征空间,在高维特征空间中对说话人的训练语音特征进行模糊聚类分析,将得到的每个类中心作为说话人的语音模型。识别时将识别矢量映射到高维空间进行匹配决策。由于核方法的引入,使得原来没有显现的特征突现出来,增加了说话人之间的可区分性。实验表明,该方法对于较短的训练语音,其识别效果优于高斯混合模型和模糊矢量量化。

A new method of speaker recognition with little training data was proposed, which used fuzzy kernel clustering to design vector quantization, and used the fuzzy-kernel vector quantization to train the speakers＇ models. By non-linear mapping, the data in original space were mapped to a high-dimensional feature space, which used the fuzzy clustering to the speakers＇ training features in the feature space, and formed the speaker＇s model with the clustering centers. The recognition was performed in the high-dimensional feature space. Because of the kernel mapping, the features inherent in the speech explored, which improved the discriminations of the different speakers. Experimental results show that this method can obtain better results than the Gaussian mixture model （GMM） and Fuzzy vector quantization method in the case of the little training data.