Alzheimer prediction from connected speech extracts: Assessments of generalization to new data
Wednesday 3 November 2021 at 11:30 AM
Réunion Zoom (below)
Many advances have been made in the early diagnosis of Alzheimer’s Disease (AD) using connected speech elicited from a picture description task. The use of hand built linguistic and acoustic features as well as Deep Learning (DL) approaches have shown promising results in the classification of AD patients. In this research, we compare both approaches on the Cookie Theft scene from the Boston Aphasia Examination with models trained with features derived from the text and audio extracts, as well as a DL approach using BERT. We train and evaluate our models on the new ADReSS challenge dataset as well as on the CCNA dataset in order to assess the generalization of the trained model on from a different dataset. A thorough evaluation of the interpretability of the models is performed to see how well each of the models learn the representations related to the disease.
(The presentation will be in French)
Enregistrement de la présentation : https://drive.google.com/file/d/1yycrOgLFHG5YuvHHhPyJDXU4maKYmNGM/view?usp=sharing
To receive weekly talk announcements, please send an e-mail to email@example.com. Simply write a message containing the single line 'subscribe ralli' (without the quotes, with a double 'l' in 'ralli').
See all the weekly talks for the year:1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022