BibTeX

@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},
}

@misc{kokkinakis-etal-2020-proceedings-305214,
	title        = {Proceedings of the LREC 2020. Workshop on: Resources and Processing of Linguistic, Para-linguistic and Extra-linguistic Data from People with Various Forms of Cognitive/Psychiatric/Developmental Impairments (RaPID-3), May 11-16, 2020, Marseille, France},
	abstract     = {RaPID-3 aims to be an interdisciplinary forum for researchers to share information, findings, methods, models and experience on the collection and processing of data produced by people with various forms of mental, cognitive, neuropsychiatric, or neurodegenerative impairments, such as aphasia, dementia, autism, bipolar disorder, Parkinson’s disease or schizophrenia. Particularly, the workshop’s focus is on creation, processing and application of data resources from individuals at various stages of these impairments and with varying degrees of severity. Creation of resources includes e.g. annotation, description, analysis and interpretation of linguistic, paralinguistic and extra-linguistic data (such as spontaneous spoken language, transcripts, eyetracking measurements, wearable and sensor data, etc). Processing is done to identify, extract, correlate, evaluate and disseminate various linguistic or multimodal phenotypes and measurements, which then can be applied to aid diagnosis, monitor the progression or predict individuals at risk.
A central aim is to facilitate the study of the relationships among various levels of linguistic, paralinguistic and extra-linguistic observations (e.g., acoustic measures; phonological, syntactic and semantic features; eye tracking measurements; sensors, signs and multimodal signals). Submission of papers are invited in all of the aforementioned areas, particularly emphasizing multidisciplinary aspects of processing such data and the interplay between clinical/nursing/medical sciences, language technology, computational linguistics, natural language processing (NLP) and computer science. The workshop will act as a stimulus for the discussion of several ongoing research questions driving current and future research by bringing together researchers from various research communities.
},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina  and Themistocleous, Charalambos  and Antonsson, Malin  and Eckerström , Marie},
	year         = {2020},
	publisher    = {European Language Resources Association (ELRA)},
	address      = {Paris},
	ISBN         = {979-10-95546-45-0},
}

@inProceedings{themistocleous-etal-2020-automatic-305224,
	title        = {Automatic analysis of voice quality and prosody in patients with Mild Cognitive Impairment.},
	abstract     = {http://demo.spraakdata.gu.se/svedk/pbl/SNL2020.pdf},
	booktitle    = {The 12th Annual Society for the Neurobiology of Language Meeting (SNL) -- virtual conference},
	author       = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios},
	year         = {2020},
}

@inProceedings{themistocleous-etal-2020-automated-305223,
	title        = {Automated speech analysis improves MCI diagnosis},
	abstract     = {Mild Cognitive Impairment (MCI) is a condition characterized by cognitive
decline greater than expected for an individual's age and education level. In this
study, we are investigating whether acoustic properties of speech production
can improve the classification of individuals with MCI from healthy controls
augmenting the Mini Mental State Examination, a traditional screening tool,
with automatically extracted acoustic information. We found that just one
acoustic feature, can improve the AUC score (measuring a trade-off between
sensitivity and specificity) from 0.77 to 0.89 in a boosting classification task.
These preliminary results suggest that computerized language analysis can
improve the accuracy of traditional screening tools},
	booktitle    = {Proceedings of the 11th Experimental Linguistics Conference (ExLing)},
	author       = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios},
	year         = {2020},
}

@inProceedings{themistocleous-etal-2020-improving-305222,
	title        = {Improving the Diagnosis of Mild Cognitive Impairment in elderly individuals using a multifactorial automatic analysis of voice quality and prosody.},
	abstract     = {http://demo.spraakdata.gu.se/svedk/pbl/AEC-30-Paper.JPG},
	booktitle    = {30th Alzheimer Europe Conference #30AEC -- virtual conference },
	author       = {Themistocleous, Charalambos and Eckerström, Marie and Lundholm Fors, Kristina and Kokkinakis, Dimitrios},
	year         = {2020},
}

@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},
}