A Comparison of Acoustic and Linguistics Methodologies for Alzheimer’s Dementia Recognition

Cummins, Nicholas; Pan, Yilin; Ren, Zhao; Fritsch, Julian; Nallanthighal, Venkata Srikanth; Christensen, Heidi; Blackburn, Daniel; Schuller, Björn W.; Magimai-Doss, Mathew; Strik, Helmer; Härmä, Aki

doi:10.21437/interspeech.2020-2635

Cited by 34 publications

(27 citation statements)

References 27 publications

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“…The top-performing model was proposed by Yuan et al [27] whose approach achieved an accuracy of 89.60% compared to the challenge baseline of 75.00%. The second placed model was proposed by us [26], and it achieved an accuracy of 85.42%, which was marginally better than the third placed model, proposed by Cummins et al [25], who achieved an accuracy of 85.20%. For the regression task of the challenge, only three participants were successful at improving the baseline RMSE score of 4.34.…”

Section: Introductionmentioning

confidence: 74%

“…Cummins et al [25], proposed a multimodal fusion system as part of their solution for the ADReSS challenge. For the audio modality, they used three types of acoustic feature representations, which included (a) the popular bag-of-audio words (BoAW) feature aggregation method for acoustic lowlevel descriptors [37], (b) an end-to-end (e2e) convolutional neural network which learns to classify using raw audio waveforms, and (c) a siamese network which learns to classify using Mel spectrogram representation of the subjects' speech signal.…”

Section: Introductionmentioning

confidence: 99%

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Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Syed

Lech

et al. 2021

IEEE Access

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Alzheimer's dementia is a progressive neurodegenerative disease that causes cognitive and physical impairment. It severely deteriorates the quality of life in affected individuals. An early diagnosis can assist immensely in better management of their healthcare needs. In recent years, there has been a renewed impetus in development of automated methods for recognition of various disorders by leveraging advancements in artificial intelligence. Here, we propose a multimodal system that can identify linguistic and paralinguistic traits of dementia using an automated screening tool. We show that bag-of-deep-neuralembeddings and ensemble learning offer a viable approach to objective assessment of dementia. The developed system is tested on the Alzheimer's Dementia Recognition Challenge dataset, where it achieved a new state-of-the-art (SOTA) performance for the classification task and matched the current SOTA for the regression task. These results highlight the efficacy of our proposed system for facilitating an early diagnosis of dementia.

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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Syed

Lech

et al. 2021

IEEE Access

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“…On the balanced DementiaBank dataset using both linguistic and paralinguistic features, an 87.5% classification accuracy was achieved using a Random Forest classifier (Farrús and Codina-Filbà, 2020 ) and an 85.2% using a fusion deep learning approach (Cummins et al, 2020 ). On a different subset of 167 samples from DementiaBank, combining linguistic and paralinguistic features yielded an 81% accuracy (Fraser et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…The English baseline classifier with all features (on the same data set as Cummins et al, 2020;Farrús and Codina-Filbà, 2020) achieved an AUC of 0.72 and accuracy of 69.7% using a LR classifier. In comparison, the English classifier with generalizable language features achieved an AUC of 0.87 and an accuracy of 76.4% using a LR model.…”

Section: Comparison To Baselinementioning

confidence: 99%

Language Impairment in Alzheimer’s Disease—Robust and Explainable Evidence for AD-Related Deterioration of Spontaneous Speech Through Multilingual Machine Learning

Lindsay

Tröger

König

2021

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is a pervasive neurodegenerative disease that affects millions worldwide and is most prominently associated with broad cognitive decline, including language impairment. Picture description tasks are routinely used to monitor language impairment in AD. Due to the high amount of manual resources needed for an in-depth analysis of thereby-produced spontaneous speech, advanced natural language processing (NLP) combined with machine learning (ML) represents a promising opportunity. In this applied research field though, NLP and ML methodology do not necessarily ensure robust clinically actionable insights into cognitive language impairment in AD and additional precautions must be taken to ensure clinical-validity and generalizability of results. In this study, we add generalizability through multilingual feature statistics to computational approaches for the detection of language impairment in AD. We include 154 participants (78 healthy subjects, 76 patients with AD) from two different languages (106 English speaking and 47 French speaking). Each participant completed a picture description task, in addition to a battery of neuropsychological tests. Each response was recorded and manually transcribed. From this, task-specific, semantic, syntactic and paralinguistic features are extracted using NLP resources. Using inferential statistics, we determined language features, excluding task specific features, that are significant in both languages and therefore represent “generalizable” signs for cognitive language impairment in AD. In a second step, we evaluated all features as well as the generalizable ones for English, French and both languages in a binary discrimination ML scenario (AD vs. healthy) using a variety of classifiers. The generalizable language feature set outperforms the all language feature set in English, French and the multilingual scenarios. Semantic features are the most generalizable while paralinguistic features show no overlap between languages. The multilingual model shows an equal distribution of error in both English and French. By leveraging multilingual statistics combined with a theory-driven approach, we identify AD-related language impairment that generalizes beyond a single corpus or language to model language impairment as a clinically-relevant cognitive symptom. We find a primary impairment in semantics in addition to mild syntactic impairment, possibly confounded by additional impaired cognitive functions.

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“…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%

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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A Comparison of Acoustic and Linguistics Methodologies for Alzheimer’s Dementia Recognition

Cited by 34 publications

References 27 publications

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Language Impairment in Alzheimer’s Disease—Robust and Explainable Evidence for AD-Related Deterioration of Spontaneous Speech Through Multilingual Machine Learning

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

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