语音的自动时间分析:一个快速和客观的管道,用于评估公开的口吃
Vishruta Yawatkar1, Ho Ming Chow2, Evan Usler2
1Center for Bioinformatics and Computational Biology, University of Delaware, 590 Avenue 1743, Suite 147, Newark, DE, 19713, USA. vishruta.yawatkar@gmail.com.
Behavior research methods
|July 18, 2025
概括
一种新方法,即语音的自动时间分析 (ATAS),通过分析停顿和声音事件,准确地测量语音流性. 这种工具有助于诊断口吃,可以区分口吃的成年人和不口吃的成年人.
科学领域:
- 语音语言病理学 语言病理学
- 计算语言学计算语言学
- 生物医学工程 生物医学工程
背景情况:
- 流利性障碍,就像发育性口吃一样,涉及诸如阻塞,重复和延长之类的语言流利性障碍.
- 准确测量口吃行为对于诊断和有效的治疗计划至关重要.
- 当前的评估方法可以从补充的客观措施中受益.
研究的目的:
- 引入和验证用于评估语音流性的自动时间语音分析 (ATAS) 方法.
- 使用ATAS量化语音时间事件,包括停顿和发音,使用ATAS.
- 评估ATAS指标与口吃严重程度的相关性,以及根据口吃状态对个人进行分类的能力.
主要方法:
- 开发了ATAS管道,以检测和量化口语阅读音频样本中的离散暂停和声音事件.
- 收集了35名英语参与者的语音数据,17名成年人口吃 (AWS) 和18名成年人不口吃 (AWNS).
- 利用机器学习分类器 (随机森林,LSTM) 来根据ATAS指标预测参与者群体状态.
主要成果:
- 与没有口吃 (AWNS) 的成年人相比,口吃 (AWS) 的成年人表现出显著更多的暂停 (语音速度较慢,总暂停时间较长,暂停数较多,平均暂停时间较长).
- ATAS衍生的暂停和语音指标与口吃的音节百分比有很强的相关性,这是口吃频率的标准衡量标准.
- ATAS指标能够准确地将参与者分为AWS和AWNS组,超过随机猜测的准确性.
结论:
- ATAS方法提供了客观的,可量化的时间语音指标,与公开口吃行为高度相关.
- ATAS为语音语言病理学家提供了一个有希望的替代和补充工具,用于评估流性障碍.
- 语音时间的自动化分析对改善口吃的诊断和管理具有重大潜力.
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