Human-Media Interaction publishes work focused on all aspects of human interaction with computers and related media. The section welcomes submissions from academic and industry researchers that seek to advance understanding and development of both human-computer interaction and human-human interaction through computational media.
https://www.frontiersin.org/journals/all/sections/human-media-interaction
Dementia is a category of neurodegenerative diseases that entails a long-term and usually gradual decrease of cognitive functioning. The main risk factor for dementia is age, and therefore its greatest incidence is amongst the elderly. Due to the severity of the situation worldwide, institutions and researchers are investing considerably in dementia prevention and early detection, focusing on disease progression. There is a need for cost-effective and scalable methods for the detection of dementia from its most subtle forms, such as the pre-clinical stage of Subjective Cognitive Impairment (SCI), to more severe conditions like Mild Cognitive Impairment (MCI) and Alzheimer's Dementia (AD) itself.
While a number of studies have investigated speech and language features for the detection of Alzheimer's Disease and mild cognitive impairment, and have proposed various signal processing and machine learning methods for this prediction task, the field still lacks balanced and standardized data sets on which these different approaches can be systematically compared.
The main objective of this Research Topic is to explore the use of speech characteristics for AD recognition using standardized data sets. We expect that this Topic will bring together groups working on this active area of research, and provide the community with the very first comprehensive comparison of different approaches to AD recognition using the speech information.
https://www.frontiersin.org/research-topics/13702/alzheimers-dementia-recognition-through-spontaneous-speech http://www.homepages.ed.ac.uk/sluzfil/ADReSS/
The code in this repository extends the manuscript submitted to this Research Topic, that draws on the ADReSS Challenge, held as part of INTERSPEECH 2020, and targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of cognitive impairment and Alzheimer's Dementia (AD):
• AD classification, for distinguishing individuals with AD from age and gender matched healthy controls.
Keywords: Speech Technology, Affective Computing, Prosodic Analysis, Machine Learning, Mental Health monitoring