@inproceedings{BraunFoerstelOppermannetal.2022, author = {Braun, Franziska and F{\"o}rstel, Markus and Oppermann, Bastian and Erzigkeit, Andreas and Hillemacher, Thomas and Lehfeld, Hartmut and Riedhammer, Korbinian}, title = {Automated Evaluation of Standardized Dementia Screening Tests}, doi = {10.48550/arXiv.2206.06208}, pages = {6}, year = {2022}, abstract = {For dementia screening and monitoring, standardized tests play a key role in clinical routine since they aim at minimizing subjectivity by measuring performance on a variety of cognitive tasks. In this paper, we report on a study that consists of a semi-standardized history taking followed by two standardized neuropsychological tests, namely the SKT and the CERAD-NB. The tests include basic tasks such as naming objects, learning word lists, but also widely used tools such as the MMSE. Most of the tasks are performed verbally and should thus be suitable for automated scoring based on transcripts. For the first batch of 30 patients, we analyze the correlation between expert manual evaluations and automatic evaluations based on manual and automatic transcriptions. For both SKT and CERAD-NB, we observe high to perfect correlations using manual transcripts; for certain tasks with lower correlation, the automatic scoring is stricter than the human reference since it is limited to the audio. Using automatic transcriptions, correlations drop as expected and are related to recognition accuracy; however, we still observe high correlations of up to 0.98 (SKT) and 0.85 (CERAD-NB). We show that using word alternatives helps to mitigate recognition errors and subsequently improves correlation with expert scores.}, language = {en} } @inproceedings{BraunErzigkeitLehfeldetal.2022, author = {Braun, Franziska and Erzigkeit, Andreas and Lehfeld, Hartmut and Hillemacher, Thomas and Riedhammer, Korbinian and Bayerl, Sebastian P. and Sojka, Petr and Kopeček, Ivan and Pala, Karel and Hor{\´a}k, Aleš}, title = {Going Beyond the Cookie Theft Picture Test}, series = {Lecture notes in computer science ; 13502}, booktitle = {Lecture notes in computer science ; 13502}, publisher = {Springer International Publishing}, isbn = {978-3-031-16270-1}, doi = {10.48550/arXiv.2206.05018}, pages = {437 -- 448}, year = {2022}, language = {en} } @inproceedings{BraunBayerlPerezToroetal.2023, author = {Braun, Franziska and Bayerl, Sebastian P. and P{\´e}rez-Toro, Paula A. and H{\"o}nig, Florian and Lehfeld, Hartmut and Hillemacher, Thomas and N{\"o}th, Elmar and Bocklet, Tobias and Riedhammer, Korbinian}, title = {Classifying Dementia in the Presence of Depression}, doi = {10.48550/arXiv.2308.08306}, pages = {5}, year = {2023}, abstract = {Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn.}, language = {en} }