使用Autoscore进行语音可理解性测试的自动评分
Kaila L Stipancic1, Tyson S Barrett2, Kris Tjaden1
1Department of Communicative Disorders and Sciences, University at Buffalo, NY.
American journal of speech-language pathology
|December 12, 2024
概括
新的Autoscore扩展可以准确地对发言可理解性测试 (SIT) 转录进行失联症研究. 这种自动化方法与人类得分器的准确性相匹配,提高了对患有脱节症的个体语音可理解性分析的效率.
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:
- 语音可理解性测试对于患有脱节症的人来说至关重要.
- 自动评分方法可以提高语音分析的效率和一致性.
- 现有的自动化工具可能需要针对特定临床试验进行改进.
研究的目的:
- 开发和评估Autoscore的扩展,这是一个自动化转录评分工具.
- 为了适应Autoscore用于评分语音可理解性测试 (SIT).
- 为了提高失节性言语的自动评分的准确性.
主要方法:
- 开发了Autoscore的三个新规则:复合,收缩和数字.
- 利用了4,642个听众评分的SIT成绩单,这些成绩单来自患有关节障碍的扬声器.
- 使用SIT扩展的Autoscore与使用类内相关性 (ICCs) 的人类得分器进行比较.
主要成果:
- 通过SIT扩展的Autoscore显示了与人类得分器 (ICCs> .97) 几乎相同的性能.
- 自动化和人类得分之间的分歧通常是轻微的.
- 与原始版本相比,扩展的Autoscore显示了不同意见的减少.
结论:
- 通过SIT扩展的Autoscore提供了与人类评分可比的准确性,用于失联症语音可理解性.
- 开发的扩展显著提高了SIT的Autoscore的实用性.
- 这种自动化工具提高了语音可理解性研究的效率和准确性.
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