Kitayama Itsuki, Hosokawa Kiyohito, Iwaki Shinobu, Yoshida Misao, Miyauchi Akira, Aruga Kenji, Kawabe Takanari, Kishikawa Toshihiro, Tanaka Hidenori, Tsuda Takeshi, Sato Takashi, Takenaka Yukinori, Ogawa Makoto, Inohara Hidenori
Department of Otorhinolaryngology and Head & Neck Surgery, The University of Osaka Graduate School of Medicine, Suita-city, Osaka, Japan.
Department of Otorhinolaryngology, Osaka Police Hospital (currently, Osaka International Medical and Science Center), Osaka-city, Osaka, Japan.
NPJ Digit Med. 2025 May 20;8(1):295. doi: 10.1038/s41746-025-01702-2.
The assessment of voice quality plays a critical role in the clinical evaluation of hoarseness. However, no study has established a highly accurate method for the auditory-perceptual judgment of vocal roughness, which is one of the major components of hoarseness. In this study, we developed a multivariate acoustic model for quantifying vocal roughness using a tailored fundamental frequency (f) estimation algorithm. The newly devised parameters enabled the classification and quantification of subharmonics, a key component of rough voice. Furthermore, we introduce the acoustic roughness index (ARI), a predictive acoustic model that integrates these parameters with existing acoustic parameters. The ARI demonstrates high diagnostic accuracy and a strong correlation with auditory-perceptual roughness, establishing it as a robust index for the evaluation of vocal roughness.
嗓音质量评估在声音嘶哑的临床评估中起着关键作用。然而，尚无研究建立一种用于听觉感知判断嗓音粗糙程度的高度准确方法，而嗓音粗糙是声音嘶哑的主要组成部分之一。在本研究中，我们使用定制的基频（f）估计算法开发了一种用于量化嗓音粗糙度的多变量声学模型。新设计的参数能够对粗糙嗓音的关键组成部分——次谐波进行分类和量化。此外，我们引入了声学粗糙度指数（ARI），这是一种将这些参数与现有声学参数相结合的预测性声学模型。ARI显示出高诊断准确性以及与听觉感知粗糙度的强相关性，使其成为评估嗓音粗糙度的可靠指标。
