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[将双通路级联模型和三角形模型整合用于大声朗读英语单词的尝试]

[An attempt to integrate the dual route cascaded model and the triangle model for reading English words aloud].

作者信息

Asakawa Shin-ichi

机构信息

Shin-ichi Asakawa (Centre for Information Sciences, Tokyo Woman's Christian University, Zempukuji, Suginami-ku, Tokyo.

出版信息

Shinrigaku Kenkyu. 2005 Feb;75(6):523-9. doi: 10.4992/jjpsy.75.523.

Abstract

In this article, I discuss the implementation of neural network models for reading English words aloud. Since 1989, there has been existing a debate about the models of reading English words aloud. One is the Dual Route Cascaded (DRC) model. The other is the Triangle model, whose original version was developed in 1989. Because there are arbitrary variables of both models, we did not decide which model gives better accounts for the numerous data given by psychological experiments and neuropsychological evidence. Therefore, in order to give a solution of this debate, an attempt to integrate both models was made. Introducing the Mixture of Experts Network, an elegant solution to overcome the arbitrariness of both models could be given. The Mixture of Expert Network can include both the models as a special case of this Network. From the Mixture of Expert Network's point of view, the difference between the Dual Route Cascaded model and the Triangle model would be able to describe as the quantitative difference of the dispersion parameters.

摘要

在本文中,我讨论了用于大声朗读英语单词的神经网络模型的实现。自1989年以来,关于大声朗读英语单词的模型一直存在争论。一种是双通路级联(DRC)模型。另一种是三角模型,其最初版本于1989年开发。由于这两种模型都存在任意变量,我们无法确定哪种模型能更好地解释心理实验和神经心理学证据给出的大量数据。因此,为了解决这场争论,我们尝试将两种模型整合起来。引入专家混合网络,可以给出一个优雅的解决方案来克服两种模型的任意性。专家混合网络可以将这两种模型都作为该网络的特殊情况包含在内。从专家混合网络的角度来看,双通路级联模型和三角模型之间的差异可以描述为离散参数的定量差异。

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