Linguistic and Audio Embedding-Based Machine Learning for Alzheimer's Dementia and Mild Cognitive Impairment Detection: Insights from the PROCESS Challenge
Singapore University of Technology and Design（新加坡科技设计大学）；Hochschule für Musik Detmold（音乐学院Detmold）
Adharsha Sam Edwin Sam Devahi, Sohail Singh Sangha, Prachee Priyadarshinee, Jithin Thilakan, Ivan Fu Xing Tan, Christopher Johann Clarke, Sou Ka Lon, Balamurali… 展开作者
Adharsha Sam Edwin Sam Devahi, Sohail Singh Sangha, Prachee Priyadarshinee, Jithin Thilakan, Ivan Fu Xing Tan, Christopher Johann Clarke, Sou Ka Lon, Balamurali B T, Yow Wei Quin, Chen Jer-Ming
Early detection of Alzheimer's Dementia (AD) and Mild Cognitive Impairment (MCI) is critical for timely intervention, yet current diagnostic approaches remain resource-intensive and invasive. Speech, encompassing both acoustic and linguistic dimensions, offers a promising non-invasive biomarker for cognitive decline. In this study, we present a machine learning framework for the PROCESS Challenge, leveraging both audio embeddings and linguistic features derived from spontaneous speech recordings. Audio representations were extracted using Whisper embeddings from the Cookie Theft description task, while linguistic features-spanning pronoun usage, syntactic complexity, filler words, and clause structure-were obtained from transcriptions across Semantic Fluency, Phonemic Fluency, and Cookie Theft picture description. Classification models aimed to distinguish between Healthy Controls (HC), MCI, and AD participants, while regression models predicted Mini-Mental State Examination (MMSE) scores. Results demonstrated that voted ensemble models trained on concatenated linguistic features achieved the best classification performance (F1 = 0.497), while Whisper embedding-based ensemble regressors yielded the lowest MMSE prediction error (RMSE = 2.843). Comparative evaluation within the PROCESS Challenge placed our models among the top submissions in regression task, and mid-range for classification, highlighting the complementary strengths of linguistic and audio embeddings. These findings reinforce the potential of multimodal speech-based approaches for scalable, non-invasive cognitive assessment and underline the importance of integrating task-specific linguistic and acoustic markers in dementia detection.
Swin-BERT: A Feature Fusion System designed for Speech-based Alzheimer's Dementia Detection
College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Sheffield（谢菲尔德大学）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）
DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer's Disease Speech (Version 1.0)
Sue & Bill Gross School of Nursing, University of California, Irvine (UCI)（加州大学厄尔布雷特分校护理学院）；Donald Bren School of Information and Computer Sciences, UCI, USA（加州大学厄尔布雷特分校信息与计算机科学学院）；Smart Forward, Rancho Palos Verdes, USA（Smart Forward公司）；Dept. of Psychiatry and Human Behavior, UCI, USA（加州大学厄尔布雷特分校精神病学与人类行为系）；Amore Senior Living, Laguna Niguel, USA（Amore Senior Living公司）；School of Medicine, University of California, Irvine（加州大学厄尔布雷特分校医学院）；Purdue University, West Lafayette, USA（普渡大学）；The George Washington University, Washington, D.C., USA（乔治华盛顿大学）
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）
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
South China University of Technology（华南理工大学）；University of Georgia（佐治亚大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）
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）
Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detection
Beijing JiaoTong University（北京交通大学）；Research Center of 6G Mobile Communications（6G移动通信研究中心）；School of Cyber Science and Engineering（网络空间与工程学院）；Huazhong University of Science and Technology（华中科技大学）
