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• W2087758640 abstract "In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20% relative is possible." @default.
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• W2087758640 date "2012-12-01" @default.
• W2087758640 modified "2023-10-18" @default.
• W2087758640 title "Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling" @default.
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• W2087758640 doi "https://doi.org/10.1109/slt.2012.6424206" @default.
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