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Real-time speech recognition and model selection via recurrent neural networks

通过循环神经网络进行实时语音识别和模型选择

基本信息

批准号：
06650401
负责人：
TAKAHASHI Haruhisa
金额：
$ 1.28万
依托单位：
The University of Electro-Communications
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-06650401/
关键词：
Speech recognition Neural networks Machine learning Probably Approximately Correct

项目摘要

We performed the study on the theme of this report by intensively investigating the theoretical base of learning. In the first year we developed a very simple recurrent neural network (VSRN) architecture which is a three-layr network and contains only self-loop recurrent connections in the hidden layr. The role of the recurrent connection is explained by the network dynamics and its function will be acquired by learning from finite examples like a mamalian action. Through the learning process some characteristic functions observed in the mamalian auditory systems are founed automatically acquired by the network. In the second year we investigated mainly the theoretical framework of how our network can learn well by proposing a new method for analysing the generalization performance. To achieve this, we undertake a comparison of learning and hypothesis testing, which leads to a novel notion of regular interpolation dimension and an ill-disposed learning algorithm that produces ill-disposed hypotheses. This unites the learning and the hypothesis testing in a common viewpoint such that the base of hypothesis testing inequalities can be directly used for estimating ill-disposed hypotheses on training examples. The regular interpolation dimension is no greater than the number of modifiable system parameters. We analyze the ill-disposed learning algorithm both in the PAC learning model and in an average-case setting to obtain more explicit bounds on learning curves and sample complexity in terms of the regular interpolation dimension, than those in terms of the VC dimension. The results are applied and extended to the other algorithm such as a Gibbs algorithm and the inconsistent learning to obtain explicit bound of the learning curves and sample complexity.

我们围绕本报告的主题进行了深入研究学习的理论基础。第一年，我们开发了一个非常简单的循环神经网络（VSRN）架构，它是一个三层网络，并且在隐藏层中仅包含自循环循环连接。循环连接的作用是通过网络动力学来解释的，其功能将通过从有限的例子（如哺乳动物的动作）中学习来获得。通过学习过程，在哺乳动物听觉系统中观察到的一些特征功能是由网络自动获取的。第二年，我们主要研究了网络如何良好学习的理论框架，提出了一种分析泛化性能的新方法。为了实现这一目标，我们对学习和假设检验进行了比较，从而产生了规则插值维度的新概念和产生不良假设的不良学习算法。这将学习和假设检验统一在一个共同的观点中，使得假设检验不等式的基础可以直接用于估计训练样本上的不良假设。常规插值维数不大于可修改的系统参数个数。我们分析了 PAC 学习模型和平均情况设置中的不良学习算法，以获得比 VC 维度更明确的学习曲线和样本复杂度的界限。结果被应用并扩展到其他算法，例如吉布斯算法和不一致学习，以获得学习曲线和样本复杂度的明确界限。