基于Mel频带能量系数和奇异值分解的声带病理识别

M. Hariharan, M. Paulraj, S. Yaacob
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引用次数: 17

摘要

许多方法已经发展到检测声带病理。在这些方法中,语音分析已被证明是一种很好的声带病理检测工具。本文提出了基于Mel频带能量系数(MFBECs)和奇异值分解(SVD)的特征提取方法,用于病理和正常语音的分类。为了从原始MFBECs特征数据集中提取最相关的信息,采用奇异值分解方法。为了进行分析,使用了来自马萨诸塞州眼耳医院(MEEI)数据库的病理和健康受试者的语音样本。使用简单的k-均值最近邻(k-NN)和基于线性判别分析(LDA)的分类器来测试基于MFBECs-SVD的特征向量的有效性。实验结果表明,所提出的特征具有很好的分类精度,可以有效地用于临床病理语音的检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition
Many approaches have been developed to detect the vocal fold pathology. Among the approaches, analysis of speech has proved to be an excellent tool for vocal fold pathology detection. This paper presents the Mel Frequency Band Energy Coefficients (MFBECs) combined with singular value decomposition (SVD) based feature extraction method for the classification of pathological or normal voice. In order to extract the most relevant information from the original MFBECs feature dataset, SVD is used. For the analysis, the speech samples of pathological and healthy subjects from the Massachusetts Eye and Ear Infirmary (MEEI) database are used. A simple k-means nearest neighbourhood (k-NN) and Linear Discriminant Analysis (LDA) based classifiers are used for testing the effectiveness of the MFBECs-SVD based feature vector. The experimental results show that the proposed features gives very promising classification accuracy and also can be effectively used to detect the pathological voices clinically.
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