Alzheimers Dementia Detection using Acoustic & Linguistic features and
Pre-Trained BERT
- URL: http://arxiv.org/abs/2109.11010v2
- Date: Fri, 24 Sep 2021 06:55:37 GMT
- Title: Alzheimers Dementia Detection using Acoustic & Linguistic features and
Pre-Trained BERT
- Authors: Akshay Valsaraj, Ithihas Madala, Nikhil Garg, Veeky Baths
- Abstract summary: This study focuses on three models for the classification task in the ADReSS (The Alzheimers 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.
- Score: 1.2125503552019503
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Alzheimers 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 Alzheimers 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.
Related papers
- Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models.
We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments.
We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
arXiv Detail & Related papers (2024-09-22T02:06:05Z) - Context-aware attention layers coupled with optimal transport domain
adaptation and multimodal fusion methods for recognizing dementia from
spontaneous speech [0.0]
Alzheimer's disease (AD) constitutes a complex neurocognitive disease and is the main cause of dementia.
We propose some new methods for detecting AD patients, which capture the intra- and cross-modal interactions.
Experiments conducted on the ADReSS and ADReSSo Challenge indicate the efficacy of our introduced approaches over existing research initiatives.
arXiv Detail & Related papers (2023-05-25T18:18:09Z) - Leveraging Pretrained Representations with Task-related Keywords for
Alzheimer's Disease Detection [69.53626024091076]
Alzheimer's disease (AD) is particularly prominent in older adults.
Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations.
This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
arXiv Detail & Related papers (2023-03-14T16:03:28Z) - Acoustic-Linguistic Features for Modeling Neurological Task Score in
Alzheimer's [1.290382979353427]
Natural language processing and machine learning provide promising techniques for reliably detecting Alzheimer's disease.
We compare and contrast the performance of ten linear regression models for predicting Mini-Mental Status exam scores.
We find that, for the given task, handcrafted linguistic features are more significant than acoustic and learned features.
arXiv Detail & Related papers (2022-09-13T15:35:31Z) - Exploring linguistic feature and model combination for speech
recognition based automatic AD detection [61.91708957996086]
Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques.
Scarcity of specialist data leads to uncertainty in both model selection and feature learning when developing such systems.
This paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders.
arXiv Detail & Related papers (2022-06-28T05:09:01Z) - Self-supervised models of audio effectively explain human cortical
responses to speech [71.57870452667369]
We capitalize on the progress of self-supervised speech representation learning to create new state-of-the-art models of the human auditory system.
We show that these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
arXiv Detail & Related papers (2022-05-27T22:04:02Z) - Continual Learning with Bayesian Model based on a Fixed Pre-trained
Feature Extractor [55.9023096444383]
Current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes.
Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning.
arXiv Detail & Related papers (2022-04-28T08:41:51Z) - A multi-stage machine learning model on diagnosis of esophageal
manometry [50.591267188664666]
The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage.
This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
arXiv Detail & Related papers (2021-06-25T20:09:23Z) - Multi-Modal Detection of Alzheimer's Disease from Speech and Text [3.702631194466718]
We propose a deep learning method that utilizes speech and the corresponding transcript simultaneously to detect Alzheimer's disease (AD)
The proposed method achieves 85.3% 10-fold cross-validation accuracy when trained and evaluated on the Dementiabank Pitt corpus.
arXiv Detail & Related papers (2020-11-30T21:18:17Z) - To BERT or Not To BERT: Comparing Speech and Language-based Approaches
for Alzheimer's Disease Detection [17.99855227184379]
Natural language processing and machine learning provide promising techniques for reliably detecting Alzheimer's disease (AD)
We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset.
We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.
arXiv Detail & Related papers (2020-07-26T04:50:47Z) - Comparing Natural Language Processing Techniques for Alzheimer's
Dementia Prediction in Spontaneous Speech [1.2805268849262246]
Alzheimer's Dementia (AD) is an incurable, debilitating, and progressive neurodegenerative condition that affects cognitive function.
The Alzheimer's Dementia Recognition through Spontaneous Speech task offers acoustically pre-processed and balanced datasets for the classification and prediction of AD.
arXiv Detail & Related papers (2020-06-12T17:51:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.