Classification of Psychogenic and Laryngeal Voice Diseases Based on Teager Energy Operator

I. Hammami
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Abstract

Among several ways of communication, the voice remains the fastest natural tool for human-to-human and human-to-machine communication. That is why the research in automatic voice pathology detection and classification area has gained much interest in the recent years. Indeed, these automatic systems may be considered as assistive tools for the physicians during the assessment stage. This latter may help them to make decision, whether the voice signal belongs to a healthy or unhealthy subject and identifies the nature of pathology. In this context, this paper provides a voice pathology detection and classification system based on wavelet analysis and Teager Energy Operator (TEO). First, we used the input voice signal that we taken from Saarbrucken Voice Database (SVD) [1], to extract a set of features. These feature vectors are fed into a Gaussian Mixture Model (GMM) [2] for the sake of classification. The obtained results are 96.66% for the detection task and 92.5 % using TEO. These results show that our proposal outperforms some state-of-art methods used in voice pathology identification.
基于青少年能量算子的心因性喉音疾病分类
在多种通信方式中,语音仍然是人与人之间和人机通信最快的自然工具。因此,近年来语音病理自动检测与分类领域的研究受到了广泛的关注。事实上,这些自动系统可以被认为是医生在评估阶段的辅助工具。后者可以帮助他们做出决定,无论语音信号属于健康还是不健康的主体,并识别病理的性质。在此背景下,本文提出了一种基于小波分析和Teager能量算子(TEO)的语音病理检测分类系统。首先,我们使用从Saarbrucken voice Database (SVD)[1]中获取的输入语音信号来提取一组特征。这些特征向量被输入到高斯混合模型(GMM)[2]中进行分类。该方法的检测结果为96.66%,TEO的检测结果为92.5%。这些结果表明,我们的建议优于语音病理识别中使用的一些最先进的方法。
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