Perez Toro PA, Rodriguez Salas D, Arias Vergara T, Bayerl SP, Klumpp P, Riedhammer KT, Schuster M, Nöth E, Maier A, Orozco Arroyave JR (2023)
Publication Type: Conference contribution
Publication year: 2023
Conference Proceedings Title: ICASSP 2023
DOI: 10.1109/ICASSP49357.2023.10095219
Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model’s decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition; then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD.
APA:
Perez Toro, P.A., Rodriguez Salas, D., Arias Vergara, T., Bayerl, S.P., Klumpp, P., Riedhammer, K.T.,... Orozco Arroyave, J.R. (2023). Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer’s Disease Using Forestnets. In ICASSP 2023. Rhodes Island, GR.
MLA:
Perez Toro, Paula Andrea, et al. "Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer’s Disease Using Forestnets." Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes Island 2023.
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