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Speech recognition using a wavelet packet adaptive network based fuzzy inference system

机译:基于小波包自适应网络的模糊推理系统的语音识别

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摘要

In this paper, an expert speech recognition system is presented. This paper especially deals with the combination of feature extraction and classification for real speech signals. A Wavelet packet adaptive network based fuzzy inference system (WPANFIS) model is developed in this study. WPANFIS consists of two layers: wavelet packet and adaptive network based fuzzy inference system. The wavelet packet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The performance of the developed system is evaluated by using noisy speech signals. Test results showing the effectiveness of the proposed speech recognition system are presented in the paper. The rate of correct classification is about 92% for the sample speech signals.
机译:本文提出了一种专家语音识别系统。本文特别研究了真实语音信号的特征提取和分类的结合。本文研究了一种基于小波包自适应网络的模糊推理系统(WPANFIS)模型。 WPANFIS由两层组成:小波包和基于自适应网络的模糊推理系统。小波包层用于时频域的自适应特征提取,由小波包分解和小波包熵组成。通过使用嘈杂的语音信号评估开发的系统的性能。测试结果表明了所提出的语音识别系统的有效性。样本语音信号的正确分类率约为92%。

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