The validity of glottal inverse filtering (GIF) to obtain a glottal flow waveform from radiated pressure signal in the presence and absence of source-filter interaction was studied systematically. A driven vocal fold surface model of vocal fold vibration was used to generate source signals. A one-dimensional wave reflection algorithm was used to solve for acoustic pressures in the vocal tract. Several test signals were generated with and without source-filter interaction at various fundamental frequencies and vowels. Linear Predictive Coding (LPC), Quasi Closed Phase (QCP), and Quadratic Programming (QPR) based algorithms, along with supraglottal impulse response, were used to inverse filter the radiated pressure signals to obtain the glottal flow pulses. The accuracy of each algorithm was tested for its recovery of maximum flow declination rate (MFDR), peak glottal flow, open phase ripple factor, closed phase ripple factor, and mean squared error. The algorithms were also tested for their absolute relative errors of the Normalized Amplitude Quotient, the Quasi-Open Quotient, and the Harmonic Richness Factor. The results indicated that the mean squared error decreased with increase in source-filter interaction level suggesting that the inverse filtering algorithms perform better in the presence of source-filter interaction. All glottal inverse filtering algorithms predicted the open phase ripple factor better than the closed phase ripple factor of a glottal flow waveform, irrespective of the source-filter interaction level. Major prediction errors occurred in the estimation of the closed phase ripple factor, MFDR, peak glottal flow, normalized amplitude quotient, and Quasi-Open Quotient. Feedback-related nonlinearity (source-filter interaction) affected the recovered signal primarily when f_o was well below the first formant frequency of a vowel. The prediction error increased when f_o was close to the first formant frequency due to the difficulty of estimating the precise value of resonance frequencies, which was exacerbated by nonlinear kinetic losses in the vocal tract.

系统地研究了声门逆滤波（GIF）在存在和不存在源-滤波器相互作用的情况下从辐射压力信号获取声门流量波形的有效性。声带振动的驱动声带表面模型用于生成源信号。使用一维波反射算法来求解声道中的声压。在各种基频和元音下，在有或没有源滤波器相互作用的情况下生成了多个测试信号。使用基于线性预测编码 (LPC)、准闭相 (QCP) 和二次规划 (QPR) 的算法以及声门上脉冲响应来对辐射压力信号进行逆滤波以获得声门流量脉冲。测试了每种算法的最大流量偏角率 (MFDR)、峰值声门流量、开相脉动系数、闭相脉动系数和均方误差恢复的准确性。还测试了算法的归一化幅度商、准开商和谐波丰富因子的绝对相对误差。结果表明，均方误差随着源-滤波器交互级别的增加而减小，这表明逆滤波算法在源-滤波器交互存在的情况下表现更好。所有声门逆滤波算法对声门流波形的开相纹波系数的预测优于闭相纹波系数，无论源滤波器交互水平如何。主要预测误差出现在闭相纹波因子、MFDR、峰值声门流量、归一化幅度商和准开商的估计中。 当_fo远低于元音的第一共振峰频率时，反馈相关的非线性（源滤波器相互作用）主要影响恢复的信号。当_fo接近第一共振峰频率时，由于难以估计共振频率的精确值，预测误差增加，而声道中的非线性动力学损失又加剧了预测误差。