In real world applications, the performances of speaker identification systems degrade due to the reduction of both the amount and the quality of speech utterance. For that particular purpose, we propose a speaker identification system where short utterances with few training examples are used for person identification. Therefore, only a very small amount of data involving a sentence of 2-4 seconds is used. To achieve this, we propose a novel raw waveform end-to-end convolutional neural network (CNN) for text-independent speaker identification. We use wavelet scattering transform as a fixed initialization of the first layers of a CNN network, and learn the remaining layers in a supervised manner. The conducted experiments show that our hybrid architecture combining wavelet scattering transform and CNN can successfully perform efficient feature extraction for a speaker identification, even with a small number of short duration training samples.