TRESTLE:可重复执行语音、文本和语言实验的工具包。

Changye Li, Weizhe Xu, Trevor Cohen, Martin Michalowski, Serguei Pakhomov
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引用次数: 0

摘要

越来越多的证据表明,机器学习和深度学习方法可以学习患有各种形式认知障碍(如痴呆症)的人与认知健康的人所使用的语言之间的细微差别。TalkBank 等宝贵的公共数据资源库使计算界的研究人员能够联合起来,相互学习,从而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存在差异,不同研究小组取得的结果很难直接进行比较。在本文中,我们将介绍 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,主要针对 TalkBank 库中的两个数据集,以痴呆症检测为示例领域。TRESTLE 在 2022 年 AAAI 健康智能国际研讨会的黑客挑战赛(Hackathon/Challenge)中成功部署,为数据预处理和选择策略提供了精确的数字蓝图,其他研究人员可通过 TRESTLE 重复使用这些策略,以寻求与同行和当前最先进(SOTA)方法相媲美的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments.

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.

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