Speech Recognition using Wavelet based Feature Extraction Techniques

P. Sangwan, Dinesh Sheoran, Saurabh Bhardwaj
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引用次数: 2

Abstract

Speech recognition by machine may be defined as the conversion of human speech signal into textual form automatically by the machine without any human intervention. Two feature extraction techniques utilizing DWT (Discrete Wavelet Transform) and WPD (Wavelet Packet Decomposition) for speech recognition are discussed in the present article. The comparison of two speech recognizer, first, based on Discrete Wavelet Transform and the second based on Wavelet Packet Decomposition, and with four classifiers has been done in this paper. The proposed method is implemented for a database consisting of ten digits and two hundred speakers, making it a database of 2000 speech samples. The results present the accuracy rate of the respective speech recognizers.
基于小波特征提取技术的语音识别
机器语音识别可以定义为在没有人为干预的情况下,机器自动将人的语音信号转换为文本形式。本文讨论了基于离散小波变换(DWT)和小波包分解(WPD)的语音识别特征提取技术。本文对基于离散小波变换和基于小波包分解的两种语音识别方法进行了比较,并对四种分类器进行了比较。该方法在一个由10位数字和200个说话人组成的数据库中实现,使其成为一个包含2000个语音样本的数据库。结果显示了各自语音识别器的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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