Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text completion and machine translation.
Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline.
We concluded that the main limitations of the field are poor standardisation, limited comparability of results, and a degree of disconnect between study aims and clinical applications.
Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation.
ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task.
While machine learning (ML) systems have produced great advances in several domains, their use in support of complex cooperative work remains a research challenge.
A machine learning model trained on a smaller feature set will reduce the memory and computational resources of an emotion recognition system which can result in lowering the barriers for use of health monitoring technology.
We present an approach to automatic detection of Alzheimer's type dementia based on characteristics of spontaneous spoken language dialogue consisting of interviews recorded in natural settings.
no code implementations • • Volha Petukhova, Andrei Malchanau, Youssef Oualil, Dietrich Klakow, Saturnino Luz, Fasih Haider, Nick Campbell, Dimitris Koryzis, Dimitris Spiliotopoulos, Pierre Albert, Nicklas Linz, Alex, Jan ersson
The corpus design is inspired by the HCRC Map Task Corpus which was initially designed to support the investigation of linguistic phenomena, and has been the focus of a variety of studies of communicative behaviour.