Notwithstanding the many years of research, more work is needed to create automatic speech recognition (ASR) systems with a close-to-human robustness against confounding factors such as ambient noise, channel distortion, etc. Whilst most work thus far focused on the improvement of ASR systems embedding Gaussian Mixture Models (GMM)s to compute the acoustic likelihoods in the states of a Hidden Markov Model (HMM), the present work focuses on the noise robustness of systems employing Reservoir Computing (RC) as an alternative acoustic modeling technique. Previous work already demonstrated good noise robustness for continuous digit recognition (CDR). The present paper investigates whether further progress can be achieved by driving reservoirs with noise-robust inputs that have been shown to raise the robustness of GMM-based systems, by introducing bi-directional reservoirs and by combining reservoirs with GMMs in a single system. Experiments on Aurora-2 demonstrate that it is indeed possible to raise the noise robustness without significantly increasing the system complexity.
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