Speech super-resolution (SR) aims to increase the sampling rate of a given speech signal by generating high-frequency components. This paper proposes a convolutional neural network (CNN) based SR model that takes advantage of information from both time and frequency domains. Specifically, the proposed CNN is a time-domain model that takes the raw waveform of low-resolution speech as the input, and outputs an estimate of the corresponding high-resolution waveform. During the training stage, we employ a cross-domain loss to optimize the network. We compare our model with several deep neural network (DNN) based SR models, and experiments show that our model outperforms existing models. Furthermore, the robustness of DNN-based models is investigated, in particular regarding microphone channels and downsampling schemes, which have a major impact on the performance of DNN-based SR models. By training with proper datasets and preprocessing, we improve the generalization capability for untrained microphone channels and unknown downsampling schemes.