基于次谐波语音信号的基频估计器比较
Comparison of fundamental frequency estimators with subharmonic voice signals
AI总结：
本文在持续元音数据集上比较 Praat、YAAPT、Harvest、CREPE 与 FCN-F0 五种基频估计器，利用估计质量分类和 SHR 评估次谐波处理能力，发现 FCN-F0 总体及次谐波场景表现最优。
摘要：
在临床语音信号分析中，对次谐波发声处理不当可能导致声学参数出现假阴性。因此，基频估计器识别言语基频的能力至关重要。本文提出一项持续元音研究，采用估计质量分类来识别次谐波误差，并使用次谐波谐波比（SHR）衡量次谐波发声的强度。研究在一个持续元音数据集上考察了五种估计器：Praat、YAAPT、Harvest、CREPE 和 FCN-F0。深度学习模型 FCN-F0 在总体准确率以及正确解析次谐波信号方面均表现最佳。CREPE 和 Harvest 也是用于持续元音分析的高性能估计器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
