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PATHOLOGICAL ASSESSMENT OF VOCAL FOLD NODULES AND POLYP VIA FRACTAL DIMENSION OF PATIENTS' VOICES

机译:通过患者语音的分形维数对人声折叠结节和息肉的病理学评估

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摘要

In this paper, we are going to evaluate the role of nonlinear feature, fractal dimension, in discriminating patients with speech disorders from normal subjects. Laryngeal pathologies usually cause an asymmetry in oscillation of vocal folds. This leads to sub-harmonics and chaos in generated voices. In such condition, using nonlinear dynamic features, like fractal dimension, seems to be efficient approach to analyze understudied voice. To calculate fractal dimension of voice sample, we exploit two methods of 'Petrosian' and 'Katz' and compare them with each other. Moreover, in order to evaluate role of frequency sub-bands in diagnosis process, voice signals are decomposed into two subbands by wavelet filter bank and fractal features extracted distinctively for each band. To simplify current work, we only conduct our experiments on discriminating nodule and polyp disorders from normal subjects. The best classification result is obtained 88.9% using combination of fractal dimensions of voices and their sub-bands.
机译:在本文中,我们将评估非线性特征,分形维数在区分正常人言语障碍患者中的​​作用。喉部病变通常会导致声带振荡不对称。这会导致次谐波和生成的声音混乱。在这种情况下,使用诸如分形维数之类的非线性动态特征似乎是分析未充分研究的语音的有效方法。为了计算语音样本的分形维数,我们利用“ Petrosian”和“ Katz”两种方法进行比较。此外,为了评估频率子带在诊断过程中的作用,语音信号通过小波滤波器组分解为两个子带,并针对每个频带分别提取分形特征。为了简化当前的工作,我们仅进行区分正常受试者的结节和息肉疾病的实验。结合语音及其子带的分形维数可获得最佳分类结果,为88.9%。

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