Exploring MMSE Score Prediction Using Verbal and Non-Verbal Cues

Farzana, Shahla; Parde, Natalie

doi:10.21437/interspeech.2020-3085

Cited by 11 publications

(9 citation statements)

References 19 publications

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“…Although we have not discuss here the results from this experiment in detail for the sake of brevity, we report that the two methods based on averaging achieved better performance than the median-based fusion, in terms of RMSE. Please note, Farzana et al [15] used the median-based fusion in their study. Amongst the simple label averaging and Pearson's score weighted averaging for label fusion, we found that the latter yielded a smaller RMSE, when labels from top-10 models were fused.…”

Section: Selection Of Fusion Methodsmentioning

confidence: 99%

“…Table 1 summarizes the results for the two tasks of the ADReSS challenge as per the proceedings of the Interspeech 2020 conference [15]- [27]. In addition to the scores, we also provide the ranking for each participating team based on the performance of their proposed model relative to the challenge baseline for the test partition.…”

Section: Introductionmentioning

confidence: 99%

“…Farzana et al [15] used a combination of acoustic and textual features to identify verbal dysfluency markers in speech and hypothesized that they can be useful for estimating the MMSE score. Further, they used a wide range of handcrafted textual features and fine-tuned contextual embedding based on the distilbert [28] to model dysfluency from speech transcripts.…”

Section: Introductionmentioning

confidence: 99%

“…†: did not beat the challenge baseline second best. The third place went to Farzana et al[15] whose model achieved an RMSE score of 4.34.…”

mentioning

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: Selection Of Fusion Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…†: did not beat the challenge baseline second best. The third place went to Farzana et al[15] whose model achieved an RMSE score of 4.34.…”

mentioning

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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“…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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