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arXiv · 2501.04789

Comparison of fundamental frequency estimators with subharmonic voice signals

Takeshi Ikuma·Melda Kunduk·Andrew J. McWhorter
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Abstract

In clinical voice signal analysis, mishandling of subharmonic voicing may cause an acoustic parameter to signal false negatives. As such, the ability of a fundamental frequency estimator to identify speaking fundamental frequency is critical. This paper presents a sustained-vowel study, which used a quality-of-estimate classification to identify subharmonic errors and subharmonics-to-harmonics ratio (SHR) to measure the strength of subharmonic voicing. Five estimators were studied with a sustained vowel dataset: Praat, YAAPT, Harvest, CREPE, and FCN-F0. FCN-F0, a deep-learning model, performed the best both in overall accuracy and in correctly resolving subharmonic signals. CREPE and Harvest are also highly capable estimators for sustained vowel analysis.

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BibTeXRIS

Takeshi Ikuma, Melda Kunduk, Andrew J. McWhorter. 2025-01-08. Comparison of fundamental frequency estimators with subharmonic voice signals. https://arxiv.org/abs/2501.04789

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