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Fast Word Detection in a Speech Using New High Speed Time Delay Neural Networks

机译:使用新型高速时延神经网络的语音快速单词检测

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摘要

This paper presents a new approach to speed up the operation of time delay neural networks for fast detecting a word in a speech. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.
机译:本文提出了一种新的方法来加速时延神经网络的操作,以快速检测语音中的单词。整个数据以长矢量收集在一起,然后作为一个输入模式进行测试。拟议的快速时延神经网络(FTDNN)在测试数据和神经网络的输入权重之间使用频域互相关。从数学和实践上证明,提出的时延神经网络所需的计算步骤数量少于常规时延神经网络(CTDNN)所需的计算步骤数量。使用MATLAB的仿真结果证实了理论计算。

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