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Deep neural network (DNN) is trained according to a mini-batch optimization based on the stochastic gradient descent algorithm. Such a stochastic learning suffers from instability in parameter updating and may easily trap into local optimum. This study deals with the stability of stochastic learning by reducing the variance of gradients in optimization procedure. We upgrade the optimization from the...
In this paper a new optimization algorithm based on Chaos Optimization algorithm(COA) combined with traditional Baum Welch (BW) method is presented for training Hidden Markov Model (HMM) for Continues speech recognition. The BW algorithm easily trapped in local optimum, which might deteriorate the speech recognition rate, while an important character of COA is global search. so we can get a globally...
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