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Assessment of disordered voices based on an optimized glottal source model

机译:基于优化的声源模型评估无序声音

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In this paper, a method for the assessment of disordered voices is proposed. A feature named mean opening quotient (MOQ) obtained from the glottal source estimation is used as an acoustic cue to summarize the degree of severity of the voice disorder. The analysis method uses the empirical mode decomposition algorithm to estimate the glottal source excitation signal from the speech signal. The logarithm of the magnitude spectrum of the speech signal is decomposed into oscillatory modes, called intrinsic mode functions, that are clustered into two classes, the spectral envelope and the harmonic component. The exploitation of the phase information jointly with the estimated harmonic component enables the estimation of the glottal source signal. An appropriate parametric model is fitted to the estimated glottal source excitation signal. The optimal parameters of the glottal source excitation model from which the MOQ is defined are obtained by using a genetic algorithm. The presented method is tested on a corpus of natural speech including the vowel [a] uttered by 22 normophonic speakers and 229 speakers with different degrees of dysphonia. Experimental results show that the proposed method is very effective for assessing the degree of severity of the voice disorder.
机译:本文提出了一种评估无序声音的方法。从声门源估计获得的名为均值商数(MOQ)的功能用作声音提示,以概括语音障碍的严重程度。分析方法使用经验模式分解算法从语音信号中估计声门源激励信号。语音信号幅度谱的对数被分解为称为固有模式函数的振荡模式,这些振荡模式分为两类,频谱包络和谐波分量。相位信息与估计的谐波分量一起的利用使得能够估计声门源信号。将适当的参数模型拟合到估计的声门源激励信号。通过使用遗传算法获得定义最小起订量的声门源激励模型的最优参数。所提出的方法是在自然语言的语料库上进行测试的,该自然语料库包括22个标准语音说话者和229个具有不同声调程度的说话者发出的元音[a]。实验结果表明,该方法对于评估语音障碍的严重程度非常有效。

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