Automatic speech recognition systems typically model the relationship between the acoustic speech signal and the phones in two separate steps: feature extraction and classifier training. In our recent works, we have shown that, in the framework of convolutional neural networks (CNN), the relationship between the raw speech signal and the phones can be directly modeled and ASR systems competitive to standard approach can be built. In this paper, we first analyze and show that, between the first two convolutional layers, the CNN learns (in parts) and models the phone-specific spectral envelope information of 2-4 ms speech. Given that we show that the CNN-based approach yields ASR trends similar to standard short-term spectral based ASR system under mismatched (noisy) conditions, with the CNN-based approach being more robust.