Parsa V, Jamieson D G
Hearing Health Care Research Unit, The University of Western Ontario, London, Canada.
J Speech Lang Hear Res. 1999 Feb;42(1):112-26. doi: 10.1044/jslhr.4201.112.
Perturbation analysis of sustained vowel waveforms is used routinely in the clinical evaluation of pathological voices and in monitoring patient progress during treatment. Accurate estimation of voice fundamental frequency (F0) is essential for accurate perturbation analysis. Several algorithms have been proposed for fundamental frequency extraction. To be appropriate for clinical use, a key consideration is that an F0 extraction algorithm be robust to such extraneous factors as the presence of noise and modulations in voice frequency and amplitude that are commonly associated with the voice pathologies under study. This work examines the performance of seven F0 algorithms, based on the average magnitude difference function (AMDF), the input autocorrelation function (AC), the autocorrelation function of the center-clipped signal (ACC), the autocorrelation function of the inverse filtered signal (IFAC), the signal cepstrum (CEP), the Harmonic Product Spectrum (HPS) of the signal, and the waveform matching function (WM) respectively. These algorithms were evaluated using sustained vowel samples collected from normal and pathological subjects. The effect of background noise and of frequency and amplitude modulations on these algorithms was also investigated, using synthetic vowel waveforms.
持续元音波形的微扰分析通常用于病理性嗓音的临床评估以及治疗期间监测患者的进展情况。准确估计嗓音基频（F0）对于准确的微扰分析至关重要。已经提出了几种用于基频提取的算法。为了适用于临床，一个关键的考虑因素是F0提取算法要对诸如噪声的存在以及语音频率和幅度的调制等无关因素具有鲁棒性，这些因素通常与所研究的语音病理学相关。这项工作分别基于平均幅度差函数（AMDF）、输入自相关函数（AC）、中心削波信号的自相关函数（ACC）、逆滤波信号的自相关函数（IFAC）、信号倒谱（CEP）、信号的谐波乘积谱（HPS）以及波形匹配函数（WM），研究了七种F0算法的性能。使用从正常和病理性受试者收集的持续元音样本对这些算法进行了评估。还使用合成元音波形研究了背景噪声以及频率和幅度调制对这些算法的影响。
