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Method for learning recurrent neural network, computer program therefor, and speech recognition device

机译:学习递归神经网络的方法,其计算机程序和语音识别装置

摘要

[Object] An object is to provide a training method of improving training of a recurrent neural network (RNN) using time-sequential data. [Solution] The training method includes a step 220 of initializing the RNN, and a training step 226 of training the RNN by designating a certain vector as a start position and optimizing various parameters to minimize error function. The training step 226 includes: an updating step 250 of updating RNN parameters through Truncated BPTT using consecutive N (N‰¥3) vectors having a designated vector as a start point and using a reference value of a tail vector as a correct label; and a first repetition step 240 of repeating the process of executing the training step by newly designating a vector at a position satisfying a prescribed relation with the tail of N vectors used at the updating step until an end condition is satisfied. The vector at a position satisfying the prescribed relation is positioned at least two vectors behind the designated vector.
机译:[目的]提供一种训练方法,其使用时序数据来改善对递归神经网络(RNN)的训练。 [解决方案]训练方法包括初始化RNN的步骤220和通过将某个向量指定为起始位置并优化各种参数以最小化误差函数来训练RNN的训练步骤226。训练步骤226包括:更新步骤250,其通过使用具有指定矢量作为起点的连续N(N≥3)个矢量,并且使用尾部矢量的参考值作为正确标签,通过截断的BPTT来更新RNN参数;第一重复步骤240,其通过在与更新步骤中使用的N个向量的尾部满足预定关系的位置处新指定向量来重复执行训练步骤的过程,直到满足结束条件为止。在满足规定关系的位置处的向量位于指定向量之后的至少两个向量处。

著录项

  • 公开/公告号JP6628350B2

    专利类型

  • 公开/公告日2020-01-08

    原文格式PDF

  • 申请/专利号JP20150096150

  • 发明设计人 神田 直之;

    申请日2015-05-11

  • 分类号G06N3/08;G06N3/04;G10L15/16;G10L15/06;

  • 国家 JP

  • 入库时间 2022-08-21 11:32:17

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