  Previous studies support the idea of merging auditory-based Gabor features
with deep learning architectures to achieve robust automatic speech
recognition, however, the cause behind the gain of such combination is still
unknown. We believe these representations provide the deep learning decoder
with more discriminable cues. Our aim with this paper is to validate this
hypothesis by performing experiments with three different recognition tasks
(Aurora 4, CHiME 2 and CHiME 3) and assess the discriminability of the
information encoded by Gabor filterbank features. Additionally, to identify the
contribution of low, medium and high temporal modulation frequencies subsets of
the Gabor filterbank were used as features (dubbed LTM, MTM and HTM
respectively). With temporal modulation frequencies between 16 and 25 Hz, HTM
consistently outperformed the remaining ones in every condition, highlighting
the robustness of these representations against channel distortions, low
signal-to-noise ratios and acoustically challenging real-life scenarios with
relative improvements from 11 to 56% against a Mel-filterbank-DNN baseline. To
explain the results, a measure of similarity between phoneme classes from DNN
activations is proposed and linked to their acoustic properties. We find this
measure to be consistent with the observed error rates and highlight specific
differences on phoneme level to pinpoint the benefit of the proposed features.
