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

Changye Li; Weizhe Xu; Trevor Cohen; Martin Michalowski; Serguei Pakhomov

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

AMIA Jt Summits Transl Sci Proc. 2023 Jun 16:2023:360-369. eCollection 2023.

Authors

Changye Li¹, Weizhe Xu², Trevor Cohen², Martin Michalowski³, Serguei Pakhomov⁴

Affiliations

¹ Institute of Health Informatics, University of Minnesota, Minneapolis, MN.
² Biomedical and Health Informatics, University of Washington, Seattle, Washington.
³ School of Nursing, University of Minnesota, Minneapolis, MN.
⁴ College of Pharmacy, University of Minnesota, Minneapolis, MN.

PMID: 37350929
PMCID: PMC10283131

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.