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Detecting cognitive decline using speech only: The ADReSSo Challenge ...
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Abstract:
Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline. This paper presents these prediction tasks in detail, describes the datasets used, and reports the results of the baseline classification and regression models we developed for each task. A combination of acoustic and linguistic features extracted directly from audio recordings, without human intervention, yielded a baseline accuracy of 78.87% for the AD classification task, an MMSE prediction root mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive decline prediction task. ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
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URL: https://arxiv.org/abs/2104.09356 https://dx.doi.org/10.48550/arxiv.2104.09356
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Temporal Integration of Text Transcripts and Acoustic Features for Alzheimer's Diagnosis Based on Spontaneous Speech
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In: Front Aging Neurosci (2021)
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On-Talk and Off-Talk Detection: A Discrete Wavelet Transform Analysis of Electroencephalogram ; 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)
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Towards Speaker Detectionusing FaceAPI Facial Movementsin Human-Machine Multiparty Dialogue
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HAIDER, FASIH. - : KTH, Skolan för datavetenskap och kommunikation (CSC), 2013
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