This paper applies a convolutional neural network (CNN) trained for automatic speech recognition (ASR) to the task of speaker identification (SID). In the CNN/i-vector front end, the sufficient statistics are collected based on the outputs of the CNN as opposed to the traditional universal background model (UBM). Evaluated on heavily degraded speech data, the CNN/i-vector front end provides performance comparable to the UBM/i-vector baseline. The combination of these approaches, however, is shown to provide improvements of 26% in miss rate to considerably outperform the fusion of two different features in the traditional UBM/i-vectors approach. An analysis of the language- and channel-dependency of the CNN/i-vector approach is also provided to highlight future research directions.