Abstract: Convolutional neural networks (CNNs), which show success in achieving translation invariance for many image processing tasks, were investigated for continuous speech recognition. Compared to deep neural networks (DNNs), which are proven to be successful in many speech recognition tasks nowadays, CNNs can reduce the neural network model sizes significantly, and at the same time achieve even a better recognition accuracy. Experiments on standard speech corpus TIMIT and conversational speech corpus show that CNNs outperform DNNs in terms of the accuracy and the generalization ability.