Transformer-based deep neural network language models for Alzheimer's disease risk assessment from targeted speech
Background We developed transformer-based deep learning models based on natural
language processing for early risk assessment of Alzheimer's disease from the picture …
language processing for early risk assessment of Alzheimer's disease from the picture …
An overview of the ADReSS-M Signal Processing Grand Challenge on Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech
The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE
International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The …
International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The …
Language impairment in Alzheimer's disease—robust and explainable evidence for ad-related deterioration of spontaneous speech through multilingual machine …
Alzheimer's disease (AD) is a pervasive neurodegenerative disease that affects millions
worldwide and is most prominently associated with broad cognitive decline, including …
worldwide and is most prominently associated with broad cognitive decline, including …
Exploring deep transfer learning techniques for Alzheimer's dementia detection
Examination of speech datasets for detecting dementia, collected via various speech tasks,
has revealed links between speech and cognitive abilities. However, the speech dataset …
has revealed links between speech and cognitive abilities. However, the speech dataset …
Evaluating voice-assistant commands for dementia detection
Early detection of cognitive decline involved in Alzheimer's Disease and Related Dementias
(ADRD) in older adults living alone is essential for develo**, planning, and initiating …
(ADRD) in older adults living alone is essential for develo**, planning, and initiating …
[PDF][PDF] Discovering invariant patterns of cognitive decline via an automated analysis of the cookie thief picture description task
Abstract The Cookie Thief task has been extensively adopted to uncover patterns
characterizing Alzheimer's Disease (AD). Yet, how findings reported on this task generalize …
characterizing Alzheimer's Disease (AD). Yet, how findings reported on this task generalize …
Rethinking domain adaptation for machine learning over clinical language
Building clinical natural language processing (NLP) systems that work on widely varying
data is an absolute necessity because of the expense of obtaining new training data. While …
data is an absolute necessity because of the expense of obtaining new training data. While …
Improving Alzheimer's disease detection for speech based on feature purification network
N Liu, Z Yuan, Q Tang - Frontiers in Public Health, 2022 - frontiersin.org
Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive
ability with illness progresses. At present, the diagnosis of AD mainly depends on the …
ability with illness progresses. At present, the diagnosis of AD mainly depends on the …
Connected speech-based cognitive assessment in Chinese and English
We present a novel benchmark dataset and prediction tasks for investigating approaches to
assess cognitive function through analysis of connected speech. The dataset consists of …
assess cognitive function through analysis of connected speech. The dataset consists of …
How you say it matters: Measuring the impact of verbal disfluency tags on automated dementia detection
Automatic speech recognition (ASR) systems usually incorporate postprocessing
mechanisms to remove disfluencies, facilitating the generation of clear, fluent transcripts that …
mechanisms to remove disfluencies, facilitating the generation of clear, fluent transcripts that …