SPE-9.6
PRIVACY SENSITIVE SPEECH ANALYSIS USING FEDERATED LEARNING TO ASSESS DEPRESSION
Suhas Bn, Saeed Abdullah, Pennsylvania State University, United States of America
Session:
Depression Detection
Track:
Speech and Language Processing
Location:
Gather Area B
Presentation Time:
Sun, 8 May, 22:00 - 22:45 China Time (UTC +8)
Sun, 8 May, 14:00 - 14:45 UTC
Sun, 8 May, 14:00 - 14:45 UTC
Session Chair:
Carol Espy-Wilson, University of Maryland
Session SPE-9
SPE-9.1: AUTOMATIC DEPRESSION DETECTION: AN EMOTIONAL AUDIO-TEXTUAL CORPUS AND A GRU/BILSTM-BASED MODEL
Ying Shen, Huiyu Yang, Lin Lin, Tongji University, China
SPE-9.2: MULTIMODAL DEPRESSION CLASSIFICATION USING ARTICULATORY COORDINATION FEATURES AND HIERARCHICAL ATTENTION BASED TEXT EMBEDDINGS
Nadee Seneviratne, Carol Espy-Wilson, University of Maryland - College Park, United States of America
SPE-9.3: THIN SLICES OF DEPRESSION: IMPROVING DEPRESSION DETECTION PERFORMANCE THROUGH DATA SEGMENTATION
Rawan Alsarrani, Alessandro Vinciarelli, University of Glasgow, United Kingdom of Great Britain and Northern Ireland; Anna Esposito, Universita' degli Studi della Campania, Italy
SPE-9.4: CLIMATE AND WEATHER: INSPECTING DEPRESSION DETECTION VIA EMOTION RECOGNITION
Wen Wu, University of Cambridge, United Kingdom of Great Britain and Northern Ireland; Mengyue Wu, Kai Yu, Shanghai Jiao Tong University, China
SPE-9.5: FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals
Vijay Ravi, Jinhan Wang, Jonathan Flint, Abeer Alwan, University of California Los Angeles, United States of America
SPE-9.6: PRIVACY SENSITIVE SPEECH ANALYSIS USING FEDERATED LEARNING TO ASSESS DEPRESSION
Suhas Bn, Saeed Abdullah, Pennsylvania State University, United States of America