Study of isolated speech recognition based on deep learning neural networks.

To improve the performance of the conventional speech recognition system, this paper introduced the autoencoder deep learning neural networks which was applied to speech recognition. The neural networks based on deep learning introduced greedy layer-wise learning algorithm by pretraining and fine-tuning. It could extract the essential features of speech signal which was needed to recognition. It could overcome the shortcomings of the conventional multilayer artificial neural networks which easily trapped into local optimum when training the model. And they needed a large number of labeled data. Then the structured alignment networks could align arbitrary frames of features to fixed frames. And it input these features to a classifier to speech recognition. This paper did some experiment with back propagation neural networks and autoencoder neural networks respectively. The results illustrate that the deep learning neural networks can outperform the conventional neural networks by 20.0% in accuracy. It is an excellent speech recognition model. [ABSTRACT FROM AUTHOR]