BibTeX

@inProceedings{kokkinakis-lundholmfors-2020-digital-295582,
	title        = {Digital Neuropsychological Tests and Biomarkers: Resources for NLP and AI Exploration in the Neuropsychological Domain},
	abstract     = {Non-invasive, time and cost-effective, easy-to-measure techniques for the early diagnosis or monitoring the progression of brain and mental disorders are at the forefront of recent research in this field. Natural Language Processing and Artificial Intelligence can play an important role in supporting and enhancing data driven approaches to improve the accuracy of prediction and classification. However, large datasets of e.g. recorded speech in the domain of cognitive health are limited. To improve the performance of existing models we need to train them on larger datasets, which could raise the accuracy of clinical diagnosis, and contribute to the detection of early signs at scale. In this paper, we outline our ongoing work to collect such data from a large population in order to support and conduct future research for modelling speech and language features in a cross-disciplinary manner. The final goal is to explore and combine linguistic with multimodal biomarkers from the same population and compare hybrid models that could increase the predictive accuracy of the algorithms that operate on them.},
	booktitle    = {CLARIN Annual Conference 2020 in Virtual Form},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina},
	year         = {2020},
}

@article{kokkinakis-lundholmfors-2020-manga-294522,
	title        = {Hur många djur du kommer på kan avslöja hur din hjärna mår},
	journal      = {Språkbruk},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina},
	year         = {2020},
	volume       = {2},
	pages        = {48--51},
}

@article{themistocleous-etal-2020-voice-295469,
	title        = {Voice quality and speech fluency distinguish individuals with Mild Cognitive Impairment from Healthy Controls},
	abstract     = {Mild Cognitive Impairment (MCI) is a syndrome characterized by cognitive decline greater than expected for an individual's age and education level. This study aims to determine whether voice quality and speech fluency distinguish patients with MCI from healthy individuals to improve diagnosis of patients with MCI. We analyzed recordings of the Cookie Theft picture description task produced by 26 patients with MCI and 29 healthy controls from Sweden and calculated measures of voice quality and speech fluency. The results show that patients with MCI differ significantly from HC with respect to acoustic aspects of voice quality, namely H1-A3, cepstral peak prominence, center of gravity, and shimmer; and speech fluency, namely articulation rate and averaged speaking time. The method proposed along with the obtainability of connected speech productions can enable quick and easy analysis of speech fluency and voice quality, providing accessible and objective diagnostic markers of patients with MCI.},
	journal      = {PloS one},
	author       = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios},
	year         = {2020},
	volume       = {15},
	number       = {7},
	pages        = {e0236009},
}