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Philipp Klumpp

4 accepted papers

2023

Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer's Disease Using Forestnets

Paula Andrea Pérez-Toro, Dalia Rodríguez-Salas, Tomás Arias-Vergara, Sebastian P. Bayerl, Philipp Klumpp, Korbinian Riedhammer +4
ICASSP 2023accepted

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…

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2021

Acoustic and Linguistic Analyses to Assess Early-Onset and Genetic Alzheimer's Disease

Paula Andrea Pérez-Toro, Juan Camilo Vásquez-Correa, Tomás Arias-Vergara, Philipp Klumpp, M. Sierra-Castrillón, M. E. Roldán-López +7
ICASSP 2021accepted

The PSEN1-E280A or Paisa mutation is responsible for most of Early-Onset Alzheimer’s (EOA) disease cases in Colombia. It affects a large kindred of over 5000 members that present the same phenotype. The most common symptoms are related to language disorders, where speech fluency is also affected due…

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2021

End-2-End Modeling of Speech and Gait from Patients with Parkinson's Disease: Comparison Between High Quality Vs. Smartphone Data

Juan Camilo Vásquez-Correa, Tomás Arias-Vergara, Philipp Klumpp, Paula Andrea Pérez-Toro, Juan Rafael Orozco-Arroyave, Elmar Nöth
ICASSP 2021accepted

Parkinson’s disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Speech and gait signals have been analyzed to detect the presence of the disease and the severity in patients. However, most studies have been performed in controlled conditions using hi…

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2018

Unobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices

Tomás Arias-Vergara, Juan Camilo Vásquez-Correa, Juan Rafael Orozco-Arroyave, Philipp Klumpp, Elmar Nöth
ICASSP 2018accepted

Parkinson's disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in noncontrolled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to…

Cited by 0Source ↗Scholar ↗