Alzheimer's disease (AD) is a progressive neurodegenerative disease most often associated with memory deficits and cognitive decline. With the aging population, there has been much interest in automated methods for cognitive impairment detection. One approach that has attracted attention in recent years is AD detection through spontaneous speech. While the results are promising, it is not certain whether the learned speech features can be generalized across languages. To fill this gap, the ADReSS-M challenge was organized. This paper presents our submission to this ICASSP-2023 Signal Processing Grand Challenge (SPGC). The model was trained on 228 English samples of a picture description task and was transferred to Greek using only 8 samples. We obtained an accuracy of 82.6% for AD detection, a root-mean-square error of 4.345 for cognitive score prediction, and ranked 2nd place in the competition out of 24 competitors.
Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models
RWTH Aachen University（亚琛工业大学）；University of Amsterdam（阿姆斯特丹大学）
Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech
Georgia Institute of Technology（佐治亚理工学院）；Shandong Mental Health Center（山东精神卫生中心）；University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；Kennesaw State University（肯尼索州立大学）
Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge
Usher Institute, Edinburgh Medical School, The University of Edinburgh（爱丁堡大学爱丁堡医学院厄舍研究所）；Carnegie Mellon University（卡内基梅隆大学）；Information Technologies Institute, CERTH（希腊研究与技术基金会信息技术研究所）
HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信学院）；University of Cambridge（剑桥大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Imperial College London（伦敦帝国学院）
Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis
Universidad del País Vasco/Euskal Herriko Unibertsitatea（巴斯克大学）；Fundació Tecnocampus（Tecnocampus基金会）；University of Vic – Central University of Catalonia（维克大学-加泰罗尼亚中央大学）；Universidad de Las Palmas de Gran Canaria（拉斯帕尔马斯大加那利大学）；Brn GuABIAN, Association for Personal Autonomy（GuABIAN 个人自主协会）
Longitudinal Speech Biomarkers for Automated Alzheimer's Detection
MIT AutoID Laboratory（麻省理工学院自动识别实验室）；Harvard University（哈佛大学）；University of Edinburgh（爱丁堡大学）；University of Central Florida（中佛罗里达大学）；University of Milano-Bicocca（米兰比可卡大学）
Dementia classification from spontaneous speech using wrapper-based feature selection
Faculty of Information Technology, University of Jyväskylä（信息科技学院，于韦斯屈莱大学）；Public Health Programme, Folkhälsan Research Center（公共卫生计划，福尔哈兰研究中心）；Faculty of Sport and Health Sciences and Gerontology Research Center, University of Jyväskylä（体育与健康科学学院及老年学研究中心，于韦斯屈莱大学）；Wellbeing Services County of Central Finland（中央芬兰福祉服务县）
Explainable and Generalisable LLM-based Cognitive Decline Detection with Spontaneous Speech
Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University Sixth Hospital（北京大学第六医院）；Peking University（北京大学）；OPPO Research Institute（OPPO研究院）；Peking University Third Hospital（北京大学第三医院）
