有序聚类方法及其在神经网络语音识别中的应用

本文提出了一种新的网络结构，我们称之为有序聚类网络。这种网络能够对语音信号进行特征提取，很好地解决神经网络语音识别中的时间规整问题。有序聚类网络从输入语音信号的特征矢量序列中撮出一组固定数目的特 矢量，然后将这组特征矢量馈入神经网络分类器进行识别。和其他的神经网络语音识别方法相比较，用这种网络进行前端处理，可以缩短后端神经网络分类器的训练和识别时间，简化经分类器的网络产高的识别率。根据该 们建立了

Sequential Cluster Method and Its Application on Neural Network Based Speech Recognition

This paper proposes a novel method named sequential cluster network to solve the time alignment problems in artificial neural network (ANN) based speech recognition. Using this network, a fixed number of feature vectors is extracted from the input speech signal, and then processed by a ANN classifier for recognition. Compared with other ANN based methods, the proposed method has many advantages such as less time taken for training and recognition with simpler ANN structure and higher accuracy. A word recognition system is established based on this method and then tested with two sets of English words. The experiment results demonstrate that the proposed method outperforms the conventional HMM and some other ANN methods.