比较五种音高估计算法在次谐波语音中的表现，发现深度学习模型最准。
Comparison of fundamental frequency estimators with subharmonic voice signals
- 用语音质量分类和次谐波比（SHR）评估算法对次谐波的识别能力。
- FCN-F0在准确率和次谐波解析上均最优，优于传统方法。
- 适合临床语音分析人员选择可靠音高估计工具。
在临床语音信号分析中，次谐波发声若处理不当可能导致声学参数误判为阴性结果。因此，音高估计算法正确识别基频的能力至关重要。本文通过持续元音数据集研究了五种估计算法：Praat、YAAPT、Harvest、CREPE 和 FCN-F0。采用估计质量分类来识别次谐波错误，并使用次谐波与谐波比（SHR）衡量次谐波发声强度。结果表明，基于深度学习的 FCN-F0 在整体准确率和次谐波信号解析能力上表现最佳；CREPE 与 Harvest 也展现出优异性能，适用于持续元音分析。
原文摘要 · Abstract (English)
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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