Fast learning algorithms for time-delay neural networks phoneme recognition

To counter the disadvantage that TDNN take up long training time, the paper puts forward several improved methods of TDNN in phoneme recognition. The comparison of proposed methods with early method shows that they are effective in increasing the convergence speed of TDNN: (1) the error backpropagation algorithm trains initially the weights of the network; (2) the single-extreme output is replaced by the double-extreme output; (3) changing the energy function updates weights according to output errors; (4) the weight update criterion of error backpropagation is changed from the average weights of all corresponding time-delay frames to the layers. All of these make the training time decrease from 23 hours and 25 minutes to 45 minutes. The convergence speed increases by tens of times when the complexity of the network increases just a little more