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计算机辅助的连续语音音节复杂性分析作为儿童语言障碍的衡量标准

Marisha Speights Atkins¹, Suzanne E Boyce², Joel MacAuslan³

¹Auburn University.

Proceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences

|October 30, 2024

概括

一种新的计算机辅助方法,即自动语法集群分析 (Automatic Syllabic Cluster Analysis),有效地分析了儿童语言中的语法复杂性. 这种方法通过计算每次发言的语法集群来识别语音产生障碍,证明了一个重要的指标.

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
儿童发展儿童发展

背景情况:

复杂音节的发音减少是儿童发言障碍的常见指标.
传统的语音转录是劳动密集型的,限制了对大型语音样本的分析.
为了有效的临床评估,需要自动化分析方法.

研究的目的:

引入和评估一种计算机辅助的方法,即自动语法集群分析 (ASCA).
评估ASCA在广泛转录,细分和计数语法单位方面的有效性.
确定语法集群计数是否是儿童语音障碍的重要指标.

主要方法:

开发自动学科集群分析 (ASCA) 工具.
在儿童语音样本中,ASCA的应用用于广泛的转录,细分和音节单位的计数.
语音障碍和没有语音障碍的儿童之间的语法集群计数的比较.

主要成果:

通过ASCA,可以快速分析语音精度.
每次发言的语法集群的数量被发现是语言障碍的重要指标.
ASCA促进了连续语音样本的更广泛的临床分析.

结论:

自动语法集群分析 (ASCA) 是分析儿童语言语法复杂性的有效工具.
ASCA为识别语音产生障碍提供了一个重要的指标.
这种方法克服了用于大规模分析的传统语音转录的局限性.

关键词:

自动化分析自动化分析孩子的言语儿童的言语连续的演讲持续的演讲.这些字母是拼音.

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