A Comparison of High Precision FO Extraction Algorithms for Sustained Vowels 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 (FO) is essential for accurate perturbation analysis. Several algorithms have been proposed for fundamental frequency extraction. To be appropriate for clinical ... Research Article
Research Article  |   February 01, 1999
A Comparison of High Precision FO Extraction Algorithms for Sustained Vowels
 
Author Affiliations & Notes
  • Vijay Parsa
    Hearing Health Care Research Unit The University of Western Ontario London, Ontario, Canada
  • Donald G. Jamieson
    Hearing Health Care Research Unit The University of Western Ontario London, Ontario, Canada
  • Contact author: Donald G. Jamieson, PhD, Director, Hearing Health Care Research Unit, The University of Western Ontario, London, Ontario, Canada N6G 1H1. Email: jamieson@audio.hhcru.uwo.ca
Article Information
Speech, Voice & Prosody / Speech / Research Articles
Research Article   |   February 01, 1999
A Comparison of High Precision FO Extraction Algorithms for Sustained Vowels
Journal of Speech, Language, and Hearing Research, February 1999, Vol. 42, 112-126. doi:10.1044/jslhr.4201.112
History: Received January 20, 1998 , Accepted September 23, 1998
 
Journal of Speech, Language, and Hearing Research, February 1999, Vol. 42, 112-126. doi:10.1044/jslhr.4201.112
History: Received January 20, 1998; Accepted September 23, 1998

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 (FO) 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 FO 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 FO 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.

Acknowledgments
This work was supported by a grant from the Natural Sciences and Engineering Research Council to Dr. D. G. Jamieson.
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