Concerning the low detection performance of the Least Significant Bit (LSB) steganography method on WAV-format speech, a speech steganalysis method based on deep residual network was proposed. First, the residual signal of the input speech signal was calculated through a fixed convolutional layer composed of multiple sets of high-pass filters, and a truncated linear unit was adopted to perform truncation to the obtained residual signal. Then, a deep network was constructed by stacking the convolutional layer and the designed residual block to extract the deep feature information of steganography. Finally, the final classification result was output by the classifier composed of the fully connected layer and Softmax layer. Experimental results under the different secret information embedding rates of two steganography methods,Hide4PGP (Hide 4 Pretty Good Privacy) and LSBmatching (Least Significant Bit matching), show that compared with the exising Convolutional Neural Network (CNN)-based steganalysis methods, the proposed method can achieve better performance, and compared with LinNet, the proposed method has the detection accuracy increased by 7 percentage points on detecting Hide4PGP with the embedding rate of 0.1 bps (bit per sample).