使用对相对基本频率的生态瞬间评估来描述声高功能.
Ahsan J Cheema1, Katherine L Marks2, Hamzeh Ghasemzadeh3
1Speech and Hearing Bioscience and Technology Program, Harvard University, 25 Shattuck St, Boston 02115, Massachusetts; Harvard Medical School, 25 Shattuck St, Boston 02115, Massachusetts; Mass General Hospital (MGH) Voice Center, 1 Bowdoin Sq, Boston 02114, Massachusetts; Eaton-Peabody Laboratories, Massachusetts Eye and Ear, 243 Charles St, Boston 02114, Massachusetts.
这项研究表明,相对基本频率 (RFF) 可以使用智能手机传感器在日常生活中识别声高功能 (VH). 在自然环境中,RFF分析准确地分类了声高功能,为语音障碍提供了一个新的生物标志物.
科学领域:
- 语音和听力科学 语言和听力科学
- 生物医学工程 生物医学工程
- 计算语言学 计算语言学
背景情况:
- 声高功能 (VH) 与常见的声音障碍有关,包括导致声病变和肌肉张力失声的声音障碍.
- 语音发声/消音期间的相对基本频率 (RFF) 与声张力有关,并将VH与实验室中的典型声音区分开来.
研究的目的:
- 评估RFF对VH的敏感性是否在自然,现场环境中持续存在.
- 探索RFF的生态瞬间评估,作为自我报告的声音努力的相关值.
- 应用机器学习来使用RFF数据对VH进行分类.
主要方法:
- 在实验室和实地设置中,使用加速度计对前部表面振动进行智能手机监测.
- 监督的机器学习算法将RFF值结合为VH分类.
- 可解释性技术以确定临床相关的RFF特征.
主要成果:
- 基于RFF的VH分类在自然环境中保持了准确性:音声创伤性VH为81.3%,非音声创伤性VH为62.5%.
- 可解释性分析揭示了VH分类的关键RFF特征.
- 在RFF和自我报告的声乐努力之间没有发现显著的相关性.
结论:
- 相对基本频率 (RFF) 在现实环境中显示出声高功能 (VH) 生物标志物的潜力.
- 机器学习与RFF监测的整合可以实现主动查和生物反语音治疗.
- 进一步的研究可以完善RFF分析,以便在语音障碍管理中临床应用.
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