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 (\textbf{T}oolkit for \textbf{R}eproducible \textbf{E}xecution of \textbf{S}peech \textbf{T}ext and \textbf{L}anguage \textbf{E}xperiments), 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.
翻译:越来越多的证据表明,机器和深层次的学习方法能够了解有各种认知障碍的人,例如痴呆症和认知健康的人所创造的语言之间的微妙差异。TalkBank等有价值的公共数据库使计算界研究人员能够联合起来,相互学习,以便在这一领域取得显著进展。然而,由于不同研究人员采用的方法和数据选择战略的差异,不同群体获得的结果难以直接比较。在本文件中,我们介绍了TRESTLE(textbf{T}Tooklkit),用于\ textbf{R}Rtextbf{E}xcultive 。TalBank Bank存储库(hackthon/Challenge)可被成功部署于challenge(HackatThon/Challenge) 中,通过国际健康战略的20-Reseal-Regereal Serviewal AA A-CEA Areal 提供国际健康前数据选择结果,通过国际健康战略的20AAAA-Real-Reditional A/Rediviewal Stal A/AA A/Real Reviewal Indeal Indeal Indial A/A/LELEA/A/LELEA/A/A/SEA ex Deviduction Develyal Develyal Shevial St 讲习班。</s>