Titre :

Alzheimer’s Dementia Detection Using Acoustic & Linguistic Features and Pre-trained BERT

Auteur(s) :

Valsaraj, Akshay [Auteur]
Madala, Ithihas [Auteur]
Garg, Nikhil [Auteur]
International Computer Science Institute [Berkeley] [ICSI]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Baths, Veeky [Auteur]

Titre de la manifestation scientifique :

2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI)

Ville :

Cario

Pays :

Égypte

Date de début de la manifestation scientifique :

2021-11-26

Titre de l’ouvrage :

2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI)

Éditeur :

IEEE

Mot(s)-clé(s) en anglais :

Speech Recognition
Human-Computer-Interaction
Computational Para-linguistics
Alzheimer

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Alzheimer’s disease is a fatal progressive brain disorder that worsens with time. It is high time we have inexpensive and quick clinical diagnostic techniques for early detection and care. In previous studies, various ...
Lire la suite >Alzheimer’s disease is a fatal progressive brain disorder that worsens with time. It is high time we have inexpensive and quick clinical diagnostic techniques for early detection and care. In previous studies, various Machine Learning techniques and Pre-trained Deep Learning models have been used in conjunction with the extraction of various acoustic and linguistic features. Our study focuses on three models for the classification task in the ADReSS (The Alzheimer’s Dementia Recognition through Spontaneous Speech) 2021 Challenge. We use the well-balanced dataset provided by the ADReSS Challenge for training and validating our models. Model 1 uses various acoustic features from the eGeMAPs feature-set, Model 2 uses various linguistic features that we generated from auto-generated transcripts and Model 3 uses the auto-generated transcripts directly to extract features using a Pre-trained BERT and TF-IDF. These models are described in detail in the models section.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet Européen :

An iono-electronic neuromorphic interface for communication with living systems

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL