Detecting Alzheimer’s Disease from Speech Using Neural Networks with Bottleneck Features and Data Augmentation
Abstract
This paper presents a method of detecting Alzheimer’s disease (AD) from the spontaneous speech of subjects in a picture description task using neural networks. This method does not rely on the manual transcriptions and annotations of a subject’s speech, but utilizes the bottleneck features extracted from audio using an ASR model. The neural network contains convolutional neural network (CNN) layers for local context modeling, bidirectional long shortterm memory (BiLSTM) layers for global context modeling and an attention pooling layer for classification. Furthermore, a masking- based data augmentation method is designed to deal with the data scarcity problem. Experiments on the DementiaBank dataset show that the detection accuracy of our proposed method is 82.59%, which is better than the baseline method based on manually-designed acoustic features and support vector machines (SVM), and achieves the state-of-the-art performance of detecting AD using only audio data on this dataset.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Liu et al. achieved F1 0.7802 on Dementia-Bank Pitt using bottleneck feature vector.
“| Liu et al. [75] | Bottleneck feature vector (depth representation feature) | speech | Dementia- Bank Pitt | F1: 0.7802 |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [75]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2680 · Cited by 3,558 · see citing works
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
Zhou, P., Shi, W. Y., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2034 · Cited by 2,108 · see citing works
Recent developments in openSMILE, the munich open-source multimedia feature extractor
Eyben, F., Weninger, F. J., Gross, F., & Schuller, B. W. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502224 · Cited by 1,265 · see citing works
Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi
McAuliffe, M., Socolof, M., Mihuc, S., Wagner, M., & Sonderegger, M. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1386 · Cited by 1,169 · see citing works
Linguistic Features Identify Alzheimer’s Disease in Narrative Speech
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150520 · Cited by 797 · see citing works
Schuller, B. W., Steidl, S., Batliner, A. M., Vinciarelli, A., Scherer, K. R., Ringeval, F., Chétouani, M., Weninger, F. J., Eyben, F., Marchi, E., et al. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-56 · Cited by 732 · see citing works
World Alzheimer Report 2018: The state of the art of dementia research: New frontiers
Patterson, C. (2018) · Cited by 586 · see citing works
The Natural History of Alzheimer's Disease: Description of Study Cohort and Accuracy of Diagnosis
Becker, J. T. (1994) · Archives of Neurology · doi:10.1001/archneur.1994.00540180063015 · Cited by 576 · see citing works
Probabilistic and Bottle-Neck Features for LVCSR of Meetings
Grézl, F., Karafiát, M., Kontar, S., & Černocký, J. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.367023 · Cited by 340 · see citing works
On the Acoustics of Emotion in Audio: What Speech, Music, and Sound have in Common
Weninger, F. J., Eyben, F., Schuller, B. W., Mortillaro, M., & Scherer, K. R. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00292 · Cited by 277 · see citing works
Feed-Forward Networks with Attention Can Solve Some Long-Term Memory ProblemsPreprint
Raffel, C., & Ellis, D. P. W. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.08756 · Cited by 272 · see citing works
Szatloczki, G., Hoffmann, I., Vincze, V., Kalman, J., & Pakaski, M. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00195 · Cited by 249 · see citing works
An Attention Pooling Based Representation Learning Method for Speech Emotion Recognition
Li, P., Song, Y., McLoughlin, I. V., Guo, W., & Dai, L.-R. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1242 · Cited by 185 · see citing works
Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models
Karlekar, S., Niu, T., & Bansal, M. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2110 · Cited by 119 · see citing works
Automatic Diagnosis of Alzheimer’s Disease Using Neural Network Language Models
Fritsch, J., Wankerl, S., & Nöth, E. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682690 · Cited by 67 · see citing works
Emotion Recognition from Human Speech Using Temporal Information and Deep Learning
Kim, J., & Saurous, R. A. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1132 · Cited by 65 · see citing works
An N-Gram Based Approach to the Automatic Diagnosis of Alzheimer’s Disease from Spoken Language
Wankerl, S., Nöth, E., & Evert, S. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1572 · Cited by 52 · see citing works
Detecting Alzheimer’s Disease Using Gated Convolutional Neural Network from Audio Data
Warnita, T., Inoue, N., & Shinoda, K. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1713 · Cited by 48 · see citing works
Automatic Hierarchical Attention Neural Network for Detecting AD
Pan, Y., Mirheidari, B., Reuber, M., Venneri, A., Blackburn, D. J., & Christensen, H. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1799 · Cited by 38 · see citing works
Detecting Alzheimer’s Disease from Continuous Speech Using Language Models
Guo, Z., Ling, Z.-H., & Li, Y. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190452 · Cited by 37 · see citing works
Self-citationBoston Diagnostic Aphasia Examination: Record Booklet
Goodglass, H. (2000) · Cited by 26 · see citing works
Dementia Detection by Analyzing Spontaneous Mandarin Speech
Liu, Z., Guo, Z., Ling, Z.-H., Wang, S., Jin, L., & Li, Y. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023041 · Cited by 19 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
AI-based tool for early detection of Alzheimer’s disease
Ul Rehman, S., Tarek, N., Magdy, C., Kamel, M., Abdelhalim, M., Melek, A., N. Mahmoud, L., & Sadek, I. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29375 · Cited by 50 · see citing works
- Authors
- Liu, Z., Guo, Z., Ling, Z., & Li, Y.
- Affiliations
- University of Science and Technology of China (CN)
- Tongji University (CN)
- Venue
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2021-06-06
- Pages
- 7323-7327
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3161417069
- Cited by
- 34
- References
- 25
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 5 · 2023: 6 · 2022: 10
- FWCI
- 2.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- Alzheimer's disease detection, speech analysis, spontaneous speech, bottleneck features, speech recognition, convolutional neural networks, bidirectional LSTM, attention pooling, data scarcity, DementiaBank, dementia diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
