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首页> 外文会议>Conference on Independent Component Analyses, Wavelets, and Neural Networks Apr 22-25, 2003 Orlando, Florida, USA >Similarity Analysis of Voice Signals Using Wavelets with Dynamic Time Warping
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Similarity Analysis of Voice Signals Using Wavelets with Dynamic Time Warping

机译：动态时间规整的小波对语音信号的相似性分析

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摘要

In this paper several wavelets are used with Dynamic Time Warping and Derivative Dynamic Time Warping techniques for analysis of sample voice signals. Statistical methods are used to determine the similarity of voice signals in the wavelet domain. Experiments conducted in this research include anal ysis of similar voice signals spoken by the same speaker, analysis of similar voice signals spoken by different speakers, and the study to determine the effect of complexity of voice signals on the similarity analysis. For the purpose of this research four subjects, two females and two males, are selected to speak the sample words. The limited number of experiments conducted in this research provided important information on the effectiveness of different wavelets and Time Warping techniques in successful analysis of similar sound signals. Some of the results are presented in tables that show the correlation between different voice signals, subjects, and/or techniques used in our analysis.
机译：在本文中，几个小波与动态时间规整和微分动态时间规整技术一起用于分析样本语音信号。统计方法用于确定小波域中语音信号的相似性。这项研究进行的实验包括分析同一说话者说出的相似语音信号，分析不同说话者说出的相似语音信号以及确定语音信号复杂度对相似性分析的影响的研究。为了本研究的目的，选择了四个对象，两个女性和两个男性来说示例词。这项研究中进行的有限数量的实验为成功分析相似的声音信号提供了有关不同小波和时间扭曲技术有效性的重要信息。一些结果显示在表中，这些表显示了我们分析中使用的不同语音信号，主题和/或技术之间的相关性。

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