  Convolutional Neural Networks (CNN) have been used in Automatic Speech
Recognition (ASR) to learn representations directly from the raw signal instead
of hand-crafted acoustic features, providing a richer and lossless input
signal. Recent researches propose to inject prior acoustic knowledge to the
first convolutional layer by integrating the shape of the impulse responses in
order to increase both the interpretability of the learnt acoustic model, and
its performances. We propose to combine the complex Gabor filter with
complex-valued deep neural networks to replace usual CNN weights kernels, to
fully take advantage of its optimal time-frequency resolution and of the
complex domain. The conducted experiments on the TIMIT phoneme recognition task
shows that the proposed approach reaches top-of-the-line performances while
remaining interpretable.
