基于RBF神经网络的抗噪语音识别

针对目前在噪音环境下语音识别系统性能较差的问题，利用RBF神经网络具有最佳逼近性能、训练速度快等特性，分别采用聚类和全监督训练算法，实现了基于RBF神经网络的抗噪语音识别系统。聚类算法的隐含层训练采用K－均值聚类算法，输出层的学习采用线性最小二乘法;全监督算法中所有参数的调整基于梯度下降法，它是一种有监督学习算法，能够选出性能优良的参数。实验表明，在不同的信噪比下，全监督算法较之聚类算法有更高的识别率。

Noise-robust speech recognition based on RBF neural network

To solve the problem that recognition rates of speech recognition systems decrease in the noisy environment presently, uses character possessing RBF neural network,which have optimal approach capability and the fast training speed,adopts clustering algorithm and whole supervision algorithm and realizes a noise-robust speech recognition system based on RBF neural network.The hidden layer training of clustering algorithm used K-means clustering algorithm and output layer learning used linear least mean square.The adjustment of the entire parameters of whole supervision algorithm is based on grads decline method.It is a kind of supervised learning algorithm and can choose excellent parameters.Experiments show that whole supervision algorithm have higher recognition rates in different SNRs than clustering algorithm.