1. A method, comprising: extracting deep scattering spectral features from an acoustic input signal to generate a deep scattering spectral feature representation of the acoustic input signal; inputting the deep scattering spectral feature representation to a speech recognition engine; decoding the acoustic input signal based on at least a portion of the deep scattering spectral feature representation input to a speech recognition engine; and outputting the decoded acoustic input signal; wherein the speech recognition engine utilizes a hybrid architecture comprising a combination of a deep neural network and a convolutional neural network to decode the acoustic input signal, and further wherein features in the deep scattering spectral feature representation that have a local correlation in frequency are fed into the convolutional neural network part of the hybrid architecture and features that remove the local correlation are fed into the deep neural network part of the hybrid architecture, and a set of probabilities output by the hybrid architecture are used to evaluate fit between a set of acoustic models and the acoustic input signal; and wherein a result of the evaluation of fit between the set of acoustic models and the acoustic input signal is output; wherein the extracting, inputting, decoding, and outputting steps are executed via a computer system comprising an acoustic signal processing unit and a memory.
2. The method of claim 1 , wherein the extracting step further comprises extracting first-order scatter features from the acoustic input signal.