基于小波神经网络的与文本无关说话人识别方法研究
Research on Text-Independent Speaker Recognition Methods Using Wavelet Neural Network
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摘要: 基于神经网络的说话人识别方法可以在一定程度上模仿人脑的功能，是说话人识别中的一种主要技术，但它通常难以确定隐层单元的数目，收敛速度慢，易于收敛到极小点。该文研究了一种用于说话人识别的小波神经网络模型，给出了网络结构和学习算法。采用Mel频率倒谱系数作为与文本无关的说话人识别的特征参数，并利用该模型进行了5个人的说话人识别实验，得到99.5%的识别率。实验结果表明，小波网络和传统的BP网络相比，训练速度和识别率都有了较大提高，具有良好的应用前景和进一步研究的价值。Abstract: The approach for speaker recognition based on neural networks is able to emulate the function of human brain in some degree, so it is a main implementation technology in the speaker recognition. But it is difficult to determine the number of hidden layer neurons, slowly convergent and easy to fall into local minimum point. The model of wavelet neural networks is studied. The structure of the network and learning algorithm are given. The recognition correctness reaches to 99.5% for 5 speakers using Mel frequency cepstral coefficient as feature parameters. The experimental at results show that the learning rate and recognition correctness are improved much compared to the BP networks. It has a good application prospect and worth to research further more.
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