基于小波网络和HMM的语音识别方法

利用隐马尔可夫模型(HMM)的动态时间序列建模能力及神经网络的模式分类能力，构成混合语音识别模型，同时考虑到语音信号的非平稳性，采用小波分析方法提取语音特征向量。通过时间规整方法，将所有具有可变长度的语音特征向量转换为相同维数的特征向量，从而简化了神经网络的结构。仿真结果表明，采用混合语音识别模型以及时间规整方法，不仅可提高识别率，同时大大缩减了训练时间，获得了很好的识别效果。

Speech Recognition Based on Wavelet Neural Network and HMM

Applying dynamic time sequence modeling ability of hidden markov model(HMM) and classifying ability of neural networks, this paper presents a hybrid speech recognition model. Considering nonstationarity of phonetic signal, wavelet analysis method is used to extract feature vectors, which are conversed into fixed dimension with time alignment measure, as a result, the structure of neural network are simplified. Simulation results show that hybrid speech recognition model can result in both higher correct recognition rate and shorter training time.