TRESTLE:用于可重复执行语音,文本和语言实验的工具包
Changye Li1, Weizhe Xu2, Trevor Cohen2
1Institute of Health Informatics, University of Minnesota, Minneapolis, MN.
概括
机器和深度学习在检测语音认知障碍方面表现有前途. 一个新的开源平台,TRESTLE,使用TalkBank数据实现可复制的痴呆症检测方法的研究和比较.
科学领域:
- 计算语言学计算语言学
- 人工智能的人工智能是人工智能.
- 神经科学是一个神经科学.
背景情况:
- 机器和深度学习模型可以区分患有痴呆症等认知障碍的个体与认知健康个体的语言模式.
- 公共可用的数据集,如TalkBank,促进了对认知障碍检测计算方法的协作研究.
- 研究方法和数据选择的变化阻碍了不同研究结果的直接比较.
研究的目的:
- 介绍一个开源平台TRESTLE (语音文本和语言实验可重复执行工具包).
- 标准化语音和语言实验的数据预处理和选择.
- 为了实现可重复的研究,并促进对认知障碍检测结果的比较.
主要方法:
- 开发了TRESTLE,这是一个开源平台,用于可重现的语音和语言实验.
- 专注于来自TalkBank存储库的两个数据集.
- 利用痴呆症检测作为一个说明性的应用领域.
- 在健康情报国际研讨会 (AAAI 2022) 的hackallenge中成功部署了TRESTLE.
主要成果:
- TRESTLE为数据预处理和选择策略提供了一个精确的数字蓝图.
- 该平台确保实验可以被其他研究人员重复使用.
- 促进与同行和当前最先进的方法进行比较的结果.
结论:
- 在认知障碍的计算研究中,TRESTLE提高了可重现性.
- 该平台支持用于分析语音和语言数据的标准化方法.
- 能够更可靠地比较痴呆症检测模型和该领域的进展.
相关概念视频
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01:13Randomized Experiments
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01:16Crossover Experiments
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01:22Language Development
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01:24Components of Language
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