Continuous digit recognition in noise: reservoirs can do an excellent job!

In this paper a formerly proposed continuous digit recognition system based on Reservoir Computing (RC) is improved in two respects: (1)the single reservoir is substituted by a stack of reservoirs, and (2)the straightforward mapping of reservoir outputs to state likelihoods is replaced by a trained non-parametric mapping. Furthermore, it is shown that a reservoir-based method can improve a model trained on clean speech to work better in a noisy condition from which it has a number of unknown digit string recordings available. The first two improvements have lead to a system that outperforms a HMM-based system with the same noise robust features as input. The model adaptation offers a promising supplementary gain at modest noise levels.