嵌入自联想神经网络的高斯混合模型说话人辨认
doi: 10.3724/SP.J.1146.2008.00275
Speaker Identification Based on GMM with Embedded AANN
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摘要: 该文提出了一种嵌入自联想神经网络的高斯混合模型，它充分利用了神经网络和高斯混合模型各自的优点，以最大似然概率(ML)为准则，把它们作为一个整体来进行训练。训练过程中，高斯混合模型和神经网络的参数交替更新。由于神经网络起到了数据整形的作用，因而提高了类内数据的相似性。实验结果表明，采用该文提出的模型在各种信噪比情况下的识别率都比基线系统有所提高，最高能达到19%。Abstract: In this paper, a modified Gaussian Mixed Model (GMM) with an embedded Auto-Associate Neural Network (AANN) is proposed. It integrates the merits of GMM and AANN. GMM and AANN as a whole are trained by means of Maximum Likelihood (ML). In the process of training, the parameters of GMM and AANN are updated alternately. AANN reshapes the distribution of the data and improves the similarity of the data in one class. Experiments show that the proposed system improves accuracy rate against baseline GMM at all SNR, maximum to 19%.
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