Continuous Digit Recognition in Noise: Reservoirs can do an excellent job!
نویسندگان
چکیده
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 HMMbased system with the same noise robust features as input. The model adaptation offers a promising supplementary gain at modest noise levels.
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