TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 14 Feb 2023.
Builds on
6 of 28 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challengecited by 104
- 2022Leakage and the Reproducibility Crisis in ML-based Sciencecited by 63
- 2020To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detectioncited by 49
- 2020A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Typecited by 9
- 2022GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Modelscited by 8
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Citing papers per year: 2024: 3; 2025: 1.
4 papers.
- 2024Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?cited by 9
- 2024Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomaliescited by 3
- 2024Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenancecited by 2
- 2025"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Testcited by 0
- 2025"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test25 Mar 2025
- 2024Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance19 Jul 2024
- 2024Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies5 Jun 2024
- 2024Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?10 Jan 2024