Alzheimer’s Dementia Recognition Through Spontaneous Speech: The ADReSS Challenge

Luz, Saturnino; Haider, Fasih; Fuente, Sofía de la; Fromm, Davida; MacWhinney, Brian

doi:10.21437/interspeech.2020-2571

Cited by 122 publications

(155 citation statements)

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“…Since these biases cannot be fully accounted for and models are hardly comparable to one another, we do not think it is meaningful to further highlight the best performing models. Such comparisons will become more meaningful when all conditions for evaluation can be aligned, such as in the ADReSS challenge [ 115 ], which provides a benchmark dataset (balanced and enhanced) and commits to a reliable study comparison.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

Self Cite

125

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Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

Self Cite

125

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“…The highest-performing classifiers for each feature type, except for the classifiers trained on x-vectors that were extracted from a system trained on just Pitt data, performed better than the highest-performing audio and text baseline classifiers that were evaluated using LOSO on the training set, which had an average accuracy of 0.565 and 0.768, respectively (Luz et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…This extraction resulted in a 34-dimensional vector for each speaker. The CLAN features were used as linguistic features in the baseline paper (Luz et al, 2020 ). In this paper, the CLAN features were combined with the BERT embeddings to explore whether combining the features improved performance.…”

Section: Methodsmentioning

confidence: 99%

“…Previous work has been done using the ADReSS dataset. Some researchers only participated in the AD classification task (Edwards et al, 2020 ; Pompili et al, 2020 ; Yuan et al, 2020 ), others only participated in the Mini-Mental State Examination (MMSE) prediction task (Farzana and Parde, 2020 ), and others participated in both tasks (Balagopalan et al, 2020 ; Cummins et al, 2020 ; Koo et al, 2020 ; Luz et al, 2020 ; Martinc and Pollak, 2020 ; Pappagari et al, 2020 ; Rohanian et al, 2020 ; Sarawgi et al, 2020 ; Searle et al, 2020 ; Syed et al, 2020 ). The best performance on the AD classification task was achieved by Yuan et al ( 2020 ), who obtained an accuracy of 89.6% on the test set using linguistic features extracted from the transcripts, as well as encoded pauses.…”

Section: Introductionmentioning

confidence: 99%

“…CLAN features were used in the baseline paper (Luz et al, 2020 ) and were combined with BERT embeddings in this paper to explore whether performance improved. Lastly, LIWC features have been used to distinguish between AD patients and healthy controls in the past (Shibata et al, 2016 ) but the dataset was very small (nine AD patients and nine healthy controls), and to our knowledge, literature using LIWC for Alzheimer's detection is limited.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech

Haulcy

Glass

2021

Front. Psychol.

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Alzheimer's Disease (AD) is a form of dementia that affects the memory, cognition, and motor skills of patients. Extensive research has been done to develop accessible, cost-effective, and non-invasive techniques for the automatic detection of AD. Previous research has shown that speech can be used to distinguish between healthy patients and afflicted patients. In this paper, the ADReSS dataset, a dataset balanced by gender and age, was used to automatically classify AD from spontaneous speech. The performance of five classifiers, as well as a convolutional neural network and long short-term memory network, was compared when trained on audio features (i-vectors and x-vectors) and text features (word vectors, BERT embeddings, LIWC features, and CLAN features). The same audio and text features were used to train five regression models to predict the Mini-Mental State Examination score for each patient, a score that has a maximum value of 30. The top-performing classification models were the support vector machine and random forest classifiers trained on BERT embeddings, which both achieved an accuracy of 85.4% on the test set. The best-performing regression model was the gradient boosting regression model trained on BERT embeddings and CLAN features, which had a root mean squared error of 4.56 on the test set. The performance on both tasks illustrates the feasibility of using speech to classify AD and predict neuropsychological scores.

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ADReFV: Face video dataset based on human‐computer interaction for Alzheimer's disease recognition

Wang

Xie

et al. 2022

Computer Animation & Virtual

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With the global aging problem becoming more and more serious, the initial screening for Alzheimer's disease (AD) will become increasingly important.We understand that facial expressions are related to the severity of dementia, but there is no face-related data in the existing Alzheimer's dataset. This article attempts to establish a facial video-based AD recognition dataset through a human-computer interaction method. This interactive task was designed for AD in attention, execution, visual space ability, facial apraxia, and facial changes in task success and failure. Using this task as the collection method, the final dataset includes 102 faces video data, specific task scores, and emotional self-evaluation. For baseline evaluation, the improved local binary pattern on three orthogonal planes and RF were employed respectively for feature extraction and classification with the 5-fold cross-validation method. The best performance was 76.00% for 3-class classification. In addition, a frame attention network based on fine-grained local region localization was proposed, which improved the accuracy of cognitive classification to 84.45%. Finally, the analysis was conducted for the association of expressions with cognition and emotion in the AD dataset. This study aims to solve the current lack of standards for AD in the field of facial recognition and contribute to future research and clinical applications.

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Alzheimer’s Dementia Recognition Through Spontaneous Speech: The ADReSS Challenge

Cited by 122 publications

References 0 publications

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech

ADReFV: Face video dataset based on human‐computer interaction for Alzheimer's disease recognition

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