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Multi-Modal Detection of Alzheimer's Disease from Speech and Text [article]

Amish Mittal, Sourav Sahoo, Arnhav Datar, Juned Kadiwala, Hrithwik Shalu, Jimson Mathew
2021 arXiv   pre-print
Reliable detection of the prodromal stages of Alzheimer's disease (AD) remains difficult even today because, unlike other neurocognitive impairments, there is no definitive diagnosis of AD in vivo.  ...  We propose a multimodal deep learning method that utilizes speech and the corresponding transcript simultaneously to detect AD.  ...  CONCLUSION In this paper, we propose an end-to-end multi-modal deep learning based method for detecting AD from speech and text.  ... 
arXiv:2012.00096v3 fatcat:tou56rwmnjchlmpvjaxjfbskpq

Multi-Modal Fusion with Gating Using Audio, Lexical and Disfluency Features for Alzheimer's Dementia Recognition from Spontaneous Speech

Morteza Rohanian, Julian Hough, Matthew Purver
2020 Interspeech 2020  
Alzheimer's Disease from speech data.  ...  This suggests Alzheimer's Disease can be detected successfully with sequence modeling of the speech data of medical sessions.  ...  Conclusions We have presented a deep multi-modal fusion model that learns the AD indicators from audio and text modalities as well as disfluency features.  ... 
doi:10.21437/interspeech.2020-2721 dblp:conf/interspeech/RohanianHP20 fatcat:dvshn4ygwfhxrfrftjktat3msi

Modular Multi-Modal Attention Network for Alzheimer's Disease Detection Using Patient Audio and Language Data

Ning Wang, Yupeng Cao, Shuai Hao, Zongru Shao, K. P. Subbalakshmi
2021 Conference of the International Speech Communication Association  
In this work, we propose a modular multi-modal architecture to automatically detect Alzheimer's disease using the dataset provided in the ADReSSo challenge.  ...  Since the dataset provides only audio samples of controls and patients, we use Google cloud-based speech-to-text API to automatically transcribe the audio files to extract text-based features.  ...  Related Work Automated detection of Alzheimer's disease has a long history of research.  ... 
doi:10.21437/interspeech.2021-2024 dblp:conf/interspeech/WangCHSS21 fatcat:fimt44gwyje47jalvdiyi5t4k4

Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs [article]

Morteza Rohanian, Julian Hough, Matthew Purver
2021 arXiv   pre-print
We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagnostic task has Alzheimer's  ...  Disease and to what degree, evaluating the ADReSSo challenge 2021 data.  ...  the severity of Alzheimer's disease [4] .  ... 
arXiv:2106.15684v1 fatcat:vti27gqwmjg4rj2ft57v3raasy

Exploring Deep Transfer Learning Techniques for Alzheimer's Dementia Detection

Youxiang Zhu, Xiaohui Liang, John A. Batsis, Robert M. Roth
2021 Frontiers in Computer Science  
Examination of speech datasets for detecting dementia, collected via various speech tasks, has revealed links between speech and cognitive abilities.  ...  Our multi-modal transfer learning introduced a slight improvement in accuracy, demonstrating that audio and text data provide limited complementary information.  ...  ± 0.01 4.20 TABLE 6 | 6 The best classification cases of the audio-based, text-based, and multi-modal models.AD: Alzheimer's disease.  ... 
doi:10.3389/fcomp.2021.624683 pmid:34046588 pmcid:PMC8153512 fatcat:7s657y4q2jaf5a6absc2sjxdhm

A Pre-trained Neural Network to Predict Alzheimer's Disease at an Early Stage

Ragavamsi Davuluri, Ragupathy Rengaswamy
2022 International Journal of Advanced Computer Science and Applications  
In this competitive world, individual has to perform lot of multi tasking to prove their efficiency, in this process the neurons in the brain gets affected after a while i.e., "Alzheimer's Disease".  ...  Alzheimer's disease (AD), which is a neuro associated disease, has become a common for past few years.  ...  Many additional deep learning methods are being utilized to diagnose Alzheimer's disease [17] and even pre-trained models can be used in the detection of Alzheimer's disease [19] .  ... 
doi:10.14569/ijacsa.2022.0130524 fatcat:rxsx5h7jxfbtfhsydt6w2hhxq4

Augmenting BERT Carefully with Underrepresented Linguistic Features [article]

Aparna Balagopalan, Jekaterina Novikova
2020 arXiv   pre-print
Fine-tuned Bidirectional Encoder Representations from Transformers (BERT)-based sequence classification models have proven to be effective for detecting Alzheimer's Disease (AD) from transcripts of human  ...  speech.  ...  The task we focus on in this work is Alzheimer's disease (AD) detection from transcripts of speech.  ... 
arXiv:2011.06153v1 fatcat:bw5rwreibnea3pioslu6hluz2e

A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis [article]

Dimitris Gkoumas, Bo Wang, Adam Tsakalidis, Maria Wolters, Arkaitz Zubiaga, Matthew Purver, Maria Liakata
2021 arXiv   pre-print
Here we propose a novel longitudinal multi-modal dataset collected from people with mild dementia and age matched controls over a period of several months in a natural setting.  ...  The multi-modal data consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extra-linguistic information such as pen strokes and keystrokes  ...  The application is designed to record three modalities: speech, typed text, hand written text.  ... 
arXiv:2109.01537v1 fatcat:t5mewwfobbezjhfenlet34vwxi

Battling Alzheimer's Disease through Early Detection: A Deep Multimodal Learning Approach

Lin Qiu, Vaibhav Rajan, Bernard C. Y. Tan
2019 International Conference on Information Systems  
Alzheimer's Disease (AD) is the sixth leading cause of death in America.  ...  Distinguishing the transitional stage -Mild Cognitive Impairment (MCI) -from cognitive decline of normal aging is vital for early detection of AD.  ...  Accurate identification of MCI can lead to early detection of AD and thereby improve quality of life for AD patients and potentially reduce the public healthcare burden of Alzheimer's Disease.  ... 
dblp:conf/icis/QiuRT19 fatcat:rn3m6csexfbwfdd2kktfgescv4

Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models [article]

Yu Qiao, Xuefeng Yin, Daniel Wiechmann, Elma Kerz
2021 arXiv   pre-print
In this paper, we combined linguistic complexity and (dis)fluency features with pretrained language models for the task of Alzheimer's disease detection of the 2021 ADReSSo (Alzheimer's Dementia Recognition  ...  through Spontaneous Speech) challenge.  ...  detection in the context of detection of Alzheimer's Disease [15] .  ... 
arXiv:2106.08689v1 fatcat:jj3cxkh2fzbxpll47j36hwjsby

Acoustic and Language Based Deep Learning Approaches for Alzheimer's Dementia Detection From Spontaneous Speech

Pranav Mahajan, Veeky Baths
2021 Frontiers in Aging Neuroscience  
Early detection of Alzheimer's Dementia (AD) from spontaneous speech overcomes the limitations of earlier approaches as it is less time consuming, can be done at home, and is relatively inexpensive.  ...  We propose a bi-modal approach for AD classification and discuss the merits and opportunities of our approach.  ...  We would like to thank the reviewers from the ADReSS challenge in Interspeech 2020 and the Frontiers research topic ADReSS for their very helpful feedback.  ... 
doi:10.3389/fnagi.2021.623607 pmid:33613269 pmcid:PMC7893079 fatcat:wpad7ptbk5dqdhsuxe6udifcfe

Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease [article]

Ning Wang, Mingxuan Chen, K.P. Subbalakshmi
2021 arXiv   pre-print
In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer's disease based on their language abilities.  ...  The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture.  ...  etc.) to detect Alzheimer's disease  ... 
arXiv:2006.14135v2 fatcat:xzdb42eabzfwpnqks6vw54iu4u

Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review [article]

Sofia de la Fuente Garcia, Craig Ritchie, Saturnino Luz
2020 arXiv   pre-print
This paper summarises current findings on the use of artificial intelligence, speech and language processing to predict cognitive decline in the context of Alzheimer's Disease, detailing current research  ...  Language is a valuable source of clinical information in Alzheimer's Disease, as it declines concurrently with neurodegeneration.  ...  Acknowledgements We thank Marshall Dozier, the Academic Support Librarian for her help with the search queries and the PROSPERO protocol.  ... 
arXiv:2010.06047v1 fatcat:gowcdpj6pfddfns3gh7amtqpze

A Multi-Modal Feature Embedding Approach to Diagnose Alzheimer Disease from Spoken Language [article]

S. Soroush Haj Zargarbashi, Bagher Babaali
2019 arXiv   pre-print
Result: This work designs a multi-modal feature embedding on the spoken language audio signal using three approaches; N-gram, i-vector, and x-vector.  ...  Alzheimer's disease is a type of dementia in which early diagnosis plays a major rule in the quality of treatment.  ...  Recent studies have used syntactical and acoustics features of a speech data to predict Alzheimer's separately.  ... 
arXiv:1910.00330v1 fatcat:gt4rvhzvtbci5h57gv7inwcjdy

Automatic measurement of propositional idea density from part-of-speech tagging

Cati Brown, Tony Snodgrass, Susan J. Kemper, Ruth Herman, Michael A. Covington
2008 Behavior Research Methods  
Aging and Alzheimer's disease. Idea density of speech and writing is well known to decline in old age, particularly in the presence of Alzheimer's disease Snowdon et al., 1996) .  ...  British Prime Minister Ramsay MacDonald (1866-1937) most likely suffered from Alzheimer's disease, and U.S.  ... 
doi:10.3758/brm.40.2.540 pmid:18522065 pmcid:PMC2423207 fatcat:ujnio2dgpjesnb3kfhitpeccsi
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