论文标题
一种新颖的语音特征融合算法，用于文本独立的扬声器识别
A Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition
论文作者
论文摘要
具有独立矢量分析（IVA）和平行卷积神经网络（PCNN）的新型语音特征融合算法是针对文本独立的说话者识别的。首先，可以从扬声器的语音中提取一些不同的特征类型，例如时域（TD）功能和频域（FD）功能，并且可以将TD和FD功能视为具有未知混合系统的独立特征组件（IFC）的线性混合物。为了估算IFC，将演讲者语音的TD和FD功能分别构建TD和FD功能矩阵。然后，通过与TD和FD功能矩阵并行获得演讲者语音的功能张量。为了增强对不同特征类型的依赖性并删除相同特征类型的冗余，独立向量分析（IVA）可用于估计具有特征张量的TD和FD特征的IFC矩阵。 IFC矩阵被用作PCNN的输入来分别提取TD和FD特征的深度特征。可以集成深度功能以获得演讲者演讲的融合功能。最后，演讲者演讲的融合功能被用作扬声器识别的深卷积神经网络（DCNN）分类器的输入。实验结果显示了提议的说话者识别系统的有效性和性能。
A novel speech feature fusion algorithm with independent vector analysis (IVA) and parallel convolutional neural network (PCNN) is proposed for text-independent speaker recognition. Firstly, some different feature types, such as the time domain (TD) features and the frequency domain (FD) features, can be extracted from a speaker's speech, and the TD and the FD features can be considered as the linear mixtures of independent feature components (IFCs) with an unknown mixing system. To estimate the IFCs, the TD and the FD features of the speaker's speech are concatenated to build the TD and the FD feature matrix, respectively. Then, a feature tensor of the speaker's speech is obtained by paralleling the TD and the FD feature matrix. To enhance the dependence on different feature types and remove the redundancies of the same feature type, the independent vector analysis (IVA) can be used to estimate the IFC matrices of TD and FD features with the feature tensor. The IFC matrices are utilized as the input of the PCNN to extract the deep features of the TD and FD features, respectively. The deep features can be integrated to obtain the fusion feature of the speaker's speech. Finally, the fusion feature of the speaker's speech is employed as the input of a deep convolutional neural network (DCNN) classifier for speaker recognition. The experimental results show the effectiveness and performances of the proposed speaker recognition system.
