2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSS-10.3
Paper Title ACOUSTIC AND LINGUISTIC ANALYSES TO ASSESS EARLY-ONSET AND GENETIC ALZHEIMER'S DISEASE
Authors Paula Andrea Pérez-Toro, University of Erlangen-Nuremberg, Germany; Juan Camilo Vásquez-Correa, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany; Tomás Arias-Vergara, Ludwig-Maximilians University, Germany; Philipp Klumpp, University of Erlangen-Nuremberg, Germany; Melissa Sierra-Castrillón, Mildred Estefania Roldán-López, David Aguillón, Liliana Hincapié-Henao, Carlos Andrés Tobón-Quintero, University of Antioquia, Colombia; Tobias Bocklet, Technische Hochschule Nürnberg, Germany; Maria Schuster, Ludwig-Maximilians University, Germany; Juan Rafael Orozco-Arroyave, University of Antioquia, Colombia; Elmar Nöth, University of Erlangen-Nuremberg, Germany
SessionSS-10: Computer Audition for Healthcare (CA4H)
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Special Sessions: Computer Audition for Healthcare (CA4H)
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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 to the difficulty to access semantic information intentionally. This study proposes the use of acoustic and linguistic methods to extract features from speech recordings and their transcriptions to discriminate people with conditions related to the Paisa mutation. We consider state-of-the-art word-embedding methods like Word2Vec and Bidirectional Encoder Representations from Transformer to process the transcripts. The speech signals are modeled by using traditional acoustic features and speaker embeddings. To the best of our knowledge, this is the first study focused on evaluating genetic Alzheimer's and EOA using acoustics and linguistics.