• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Wavelet transform approach for adaptive filtering with application to fuzzy neural network based speech recognition.
【24h】

Wavelet transform approach for adaptive filtering with application to fuzzy neural network based speech recognition.

机译：小波变换的自适应滤波方法及其在基于模糊神经网络的语音识别中的应用。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There is an increasing interest in the field of speech recognition because of expanding use of multimedia applications. A number of speech recognition algorithms are available in the literature. The objective of this dissertation is to develop a speech recognition algorithm which can recognize various phonemes. This objective has been accomplished. The proposed algorithm has three stages; adaptive filtering, wavelet transform, and neural network learning. In the adaptive filter stage, the least mean square algorithm is modified in order to find the optimal solution for adaptive filter coefficients from input speech. In the second stage, the input speech signal can be represented in terms of a wavelet expansion. It uses a combination of the coefficients of the wavelet function, and data operations can be performed using the wavelet transform. In the third stage, the neural network improves the intelligence of systems working in an uncertain environment. Neural network consists of two phases, training and recognition. Back-propagation training is utilized in this work. The learning algorithms are used for adjusting coefficients and parameters to approximate desired sets of inputs. For the neural network to find the vector parameters, a learning algorithm was developed based on the delta learning rule and the modified conjugate gradient methods.; The software implementation of the algorithm has been made using Microsoft Visual C. The algorithms are tested by making a database of various phonemes spoken by different individuals. The proposed algorithm is suitable for use under limited noise conditions.
机译：由于多媒体应用的广泛使用，对语音识别领域的兴趣日益浓厚。文献中提供了许多语音识别算法。本文的目的是开发一种可以识别各种音素的语音识别算法。这个目标已经实现。该算法分为三个阶段。自适应滤波，小波变换和神经网络学习。在自适应滤波器阶段，对最小均方算法进行修改，以便从输入语音中找到自适应滤波器系数的最佳解。在第二阶段，输入语音信号可以用小波扩展表示。它使用小波函数系数的组合，并且可以使用小波变换执行数据运算。在第三阶段，神经网络提高了在不确定环境中工作的系统的智能性。神经网络包括训练和识别两个阶段。这项工作利用了反向传播训练。学习算法用于调整系数和参数，以近似所需的输入集。为了让神经网络找到矢量参数，基于增量学习规则和改进的共轭梯度法，开发了一种学习算法。该算法的软件实现已使用Microsoft Visual C进行。通过建立由不同人员说出的各种音素的数据库来测试算法。所提出的算法适合在有限的噪声条件下使用。

著录项

  • 作者

    Jung, Byung-Chul.;

  • 作者单位

    Wayne State University.;

  • 授予单位 Wayne State University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号