TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments

Changye Li, T. Cohen, Martin Michalowski, Serguei V. S. Pakhomov
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Abstract

The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals. Valuable public data repositories such as TalkBank have made it possible for researchers in the computational community to join forces and learn from each other to make significant advances in this area. However, due to variability in approaches and data selection strategies used by various researchers, results obtained by different groups have been difficult to compare directly. In this paper, we present TRESTLE (Toolkit for Reproducible Execution of Speech Text and Language Experiments), an open source platform that focuses on two datasets from the TalkBank repository with dementia detection as an illustrative domain. Successfully deployed in the hackallenge (Hackathon/Challenge) of the International Workshop on Health Intelligence at AAAI 2022, TRESTLE provides a precise digital blueprint of the data pre-processing and selection strategies that can be reused via TRESTLE by other researchers seeking comparable results with their peers and current state-of-the-art (SOTA) approaches.
TRESTLE:语音,文本和语言实验的可重复执行工具包
越来越多的证据表明,机器和深度学习方法可以学习到患有各种认知障碍(如痴呆症)的人与认知健康的人之间语言的细微差异。像TalkBank这样有价值的公共数据库使得计算社区的研究人员能够联合起来,相互学习,在这一领域取得重大进展。然而,由于不同研究人员使用的方法和数据选择策略的可变性,不同群体获得的结果很难直接比较。在本文中,我们介绍了TRESTLE(可重复执行语音文本和语言实验的工具包),这是一个开源平台,专注于来自TalkBank存储库的两个数据集,并将痴呆检测作为示例域。在AAAI 2022年健康智能国际研讨会的黑客挑战赛(Hackathon/Challenge)中成功部署,TRESTLE提供了数据预处理和选择策略的精确数字蓝图,可以通过TRESTLE被其他寻求与同行和当前最先进(SOTA)方法比较结果的研究人员重用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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