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基于MFCC的机器学习用于非侵入性检测儿童的声结节

Wen Hu1, Yirun Jiang2, Zufei Li3

  • 1School of Basic Medical Sciences, Peking University, Beijing, China; Department of Otolaryngology, Head and Neck Surgery, Capital Center for Children's Health, Capital Medical University, Capital Institute of Pediatrics, Beijing 100020, China.

Journal of voice : official journal of the Voice Foundation
|December 11, 2025
PubMed
概括

梅尔频率塞普斯特拉系数 (MFCC) 声学特征可以准确地检测儿科声结节,而不是侵入性. 这种基于声音的方法对查和监测患有这种常见语音障碍的儿童有希望.

关键词:
机器学习 机器学习梅尔频率的塞普斯特拉尔系数儿科 儿科 儿科声乐折叠 声乐折叠 声乐折叠 声乐折叠

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