Reverse-Speech-Finder: A Neural Network Backtracking Architecture for Generating Alzheimer's Disease Speech Samples and Improving Diagnosis Performance

Jacqueline CK Lam; Lawrence YL Cheung; Victor OK Li; Yang Han

REVIEW 4 major objections 6 minor 34 references

Reverse-Speech-Finder: A Neural Network Backtracking Architecture for Generating Alzheimer's Disease Speech Samples and Improving Diagnosis Performance

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Reverse-Speech-Finder backtracks through a neural network to find the speech tokens most predictive of Alzheimer's disease, and using them to generate synthetic speech improves diagnostic accuracy by 3.5% over a SHAP-based approach.

desk verdict A real augmentation result wrapped around a marker-discovery method whose central backtracking step is written as a linear pass through a transformer — that pass is not valid as stated. read the letter →

arxiv 2505.17477 v1 pith:DSTMTW5Q submitted 2025-05-23 cs.LG cs.SDeess.AS

Victor OK Li , Yang Han , Jacqueline CK Lam , Lawrence YL Cheung This is my paper · ORCID

classification cs.LGcs.SDeess.AS

keywords Alzheimer'sdiseasedetectionspeechmarkersneuralnetworkbacktrackingcausaltracingsyntheticgenerationmodelinterpretabilitylargelanguagemodelsdataaugmentation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces Reverse-Speech-Finder (RSF), a method that identifies the speech tokens most responsible for an Alzheimer's disease (AD) classifier's decisions by backtracking from the classifier's most influential neurons back to the input tokens. The method then uses those tokens to generate new synthetic speech samples, which are added to the training data. On the Pitt speech corpus, this improves AD diagnosis accuracy by 3.5% and F1-score by 3.2% over a SHAP-based approach, while surfacing markers such as 'dishes', 'washing', and 'fragmented sentences' that standard interpretability methods do not surface. The aim is to address both the scarcity of AD speech data and the limited interpretability of deep learning diagnostic models. If the method works as claimed, it turns a black-box classifier into a generator of clinically meaningful speech markers.

What carries the argument

The central mechanism is a backtracking algorithm (Algorithm 1) that propagates causal importance from selected hidden states back to input tokens. Starting from the MPNs found by causal tracing, the algorithm iterates from the last layer to the input layer, computing for each unit a score that is the weighted sum, over all connected MPNs in the next layer, of the indirect effect and the next-layer score, using only the fitted model weights as connection strengths. The resulting input-token scores identify which tokens most likely activate the AD-predicting neurons, and these tokens are grouped into speech markers at word and category level. The same scores also weight the markers when sampling which ones to inject into generated speech, so that the most probable markers dominate the synthetic data.

What would settle it

Train the same classifier on the Pitt transcripts with the AD/non-AD labels randomly permuted, run the full RSF backtracking, and generate speech from the resulting markers; if that shuffled-label speech improves classification on the real held-out test set as much as the genuine RSF markers do, the backtracking is not extracting AD-specific signal.

Watch

Extended reading notes

Core claim

The paper's central claim is that the speech markers most predictive of AD are exactly the tokens that most strongly activate the neurons most predictive of AD, and that these tokens can be recovered by propagating importance backward through the fine-tuned model. RSF locates those 'most probable neurons' (MPNs) using causal tracing, which corrupts known AD-related markers in the input and measures how restoring each intermediate hidden state changes the AD prediction probability. It then backtracks from the MPNs to the input layer, scoring each input token by the weighted sum of the indirect effects of the MPNs it connects to, producing the 'most probable speech tokens' (MPTs). The corresponding 'most probable speech markers' (MPMs) are used to generate new speech transcripts with a language model. The paper reports that these RSF markers differ substantially from SHAP markers and yield better diagnostic performance when used for data generation.

Load-bearing premise

The load-bearing assumption is that importance flows linearly backward through the network weights, so the token scores computed by the backtracking algorithm faithfully represent how strongly each input token activates the AD-predicting neurons; if attention and nonlinearities matter, the identified 'most probable markers' could be artifacts of that linear approximation.

Editorial extensions

If this is right

RSF-based speech generation outperforms SHAP and Integrated Gradients baselines for AD classification on the Pitt corpus, attaining 85.6% accuracy and 86.9% F1 with GPT-2.
RSF identifies word-level markers such as 'dishes', 'water', 'cookie', and 'washing', and category-level markers such as 'fragmented sentences' and 'repetition of words/phrases', that SHAP does not surface.
The method requires only a fine-tuned transformer classifier and a data generator, so it can be carried over to other classification tasks with scarce labeled samples.
Generated speech that emphasizes the most probable markers expands the training set and lessens the small-sample problem for speech-based AD diagnostics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the same backtracking recipe could be applied to any classification task whose inputs can be tokenized, turning RSF into a general-purpose marker-discovery tool rather than an AD-specific one.
The paper leaves the number and threshold of MPNs unspecified, so the reported gains could be sensitive to that choice; a sensitivity analysis would sharpen the claim.
If the linear backtracking approximation is valid, the markers identified on the Pitt corpus should transfer to other dementia speech datasets, which an independent cross-dataset replication could test.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The genuinely new part of this paper is the application of the Reverse-Gene-Finder backtracking idea to speech transcripts: fine-tune a classifier, find important neurons by causal tracing, propagate scores backward to tokens, use those tokens to prompt GPT-4o to generate training samples, and measure held-out performance. The empirical part is real and better than the average arXiv submission. The evaluation uses 390 Pitt samples, two backbones, five runs, and a Wilcoxon p=0.03 for RSF over SHAP on GPT-2. Marker lists are concrete, and the comparison with SHAP markers is useful. The held-out evaluation is on real data, not on generated data, and the paper states its main limitation honestly.

The soft spot is the central marker-discovery claim. Algorithm 1 propagates token scores through a fixed weight matrix at each layer. That is not a valid backward pass for a transformer: attention weights depend on the input token at each position, and the residual stream includes layer norms and MLP nonlinearities. There is no input-independent W of the kind Algorithm 1 assumes. The paper never specifies how many MPNs are selected or at what threshold, and the IE values are scalars attached to hidden states, not positions. More importantly, the authors do not validate the backtracking scores against a ground-truth measure of token influence, e.g., perturbing individual tokens and comparing logit changes. Without that validation, the 'most probable markers' may be artifacts of a linearization, and the 3.5%/3.2% generation improvement does not isolate the backtracking step. A weaker marker-selection rule could still produce useful augmentation samples.

The stress-test note is right. I also share the reader's concerns: no sensitivity analyses for noise variance, corrupted-sample count, or sequence length; no code release; GPT-4o generation is closed and not reproducible; and the marker lists come from a single Cookie Theft dataset. The marker scores are computed from the model's own weights, so the circularity burden is real, though the held-out classification improvement gives some independence.

What is new is the speech application and the specific marker lists. The framework itself is an incremental adaptation of ROME and the authors' Reverse-Gene-Finder. The citation pattern is appropriate.

For whom? A reader working on LLM-based data augmentation for clinical NLP, or teaching interpretability, could get useful material from the empirical comparison and the clear failure mode of the backtracking step. I would not cite it in its current form. It does deserve peer review: the problem is important, the empirical comparison is genuine, and a careful reviewer could push the authors to validate the backtracking or reframe the paper as an augmentation study. That is a major revision, not a desk reject.

Referee Report

4 major / 6 minor

Summary. The paper introduces Reverse-Speech-Finder (RSF), a three-stage pipeline that fine-tunes BERT or GPT-2 for Alzheimer's disease (AD) classification on speech transcripts, identifies 'most probable neurons' (MPNs) via causal tracing, backtracks through model weights to identify 'most probable tokens' (MPTs) and corresponding speech markers (MPMs), and then uses those markers with GPT-4o to generate synthetic transcripts for data augmentation. The authors report accuracy and F1 improvements over SHAP- and IG-based generation on the Pitt Corpus, and qualitatively list discovered word- and category-level markers. The central claim is that the backtracking step reveals the most probable AD speech markers and that augmenting with these markers improves diagnostic performance.

Significance. If the backtracking scores genuinely captured token-level causal influence, RSF would be a useful interpretability and data-augmentation tool for low-resource medical NLP. The paper has real strengths: the evaluation is on held-out real speech transcripts, SHAP/IG baselines are included, both BERT and GPT-2 backbones are tested, and the authors openly acknowledge limitations and ethical risks. However, the central mechanism is not validated: the backtracking algorithm has no demonstrated validity for transformer models, several load-bearing design choices are unspecified, and the reported statistical evidence is thin. The manuscript's contribution is therefore not yet established.

major comments (4)

[§2.1.3 and Appendix A, Algorithm 1] The backtracking score update, s^i_l = sum_k W^l_{i,k}(IE(hat h^{l+1}_k)+s^{l+1}_k), treats the transformer as a fixed linear network. In a transformer, the mapping from layer l to layer l+1 is input-dependent through attention softmax weights, MLP nonlinearities, layer norms, and residual additions; there is no fixed matrix W^l that is independent of the token at position i. Moreover, the indirect-effect values IE are scalars attached to hidden states, not to positions, so the positional indexing used in Algorithm 1 is undefined. The paper should validate the backtracking scores against token-level perturbation, such as leave-one-token-out logit changes or gradient-based attribution; without such validation, the identified MPTs and MPMs may be artifacts of an invalid linearization rather than genuine AD markers.
[§3.3] The selection of MPNs is never specified: the paper does not state how many MPNs are chosen, what score threshold is used, or how the number of MPNs affects the results. Other causal-tracing hyperparameters are fixed without justification or sensitivity analysis: noise variance 1, 10 corrupted samples per input, maximum sequence length 512, GPT-4o sampling temperature 1, and K-means cluster count. Because the identified markers and the downstream generation all depend on these choices, the main results are not reproducible as written, and the claim that the markers are 'most probable' is not robustly supported. At minimum, report the MPN selection criterion and run an ablation or sensitivity analysis over corruption noise, sample count, and sequence length.
[§3.2] The data-split procedure risks subject-level leakage. The paper states that longitudinal speech samples were treated as cross-sectional, and then a stratified 80/10/10 split of all speech samples was performed. If the same participant contributes multiple transcripts that fall into different splits, the held-out test set is not subject-independent, and diagnostic accuracy can be inflated by speaker-specific memorization. The authors need to split by participant (e.g., group by subject before stratification) or explicitly confirm that no participant appears in more than one split.
[§4.1 and Table 1] The statistical evidence for the claimed superiority of RSF is thin. The only significance statement is one Wilcoxon signed-rank test over five runs (p=0.03) comparing GPT-2 RSF against SHAP-based generation; no confidence intervals, effect sizes, per-run results, or multiple-comparison corrections are reported, and no significance test is given for BERT. With five runs and multiple configurations, a single p=0.03 is insufficient to support the claim that RSF 'significantly outperforms' the baselines. The authors should report per-run distributions with confidence intervals and perform statistical tests for both models, or temper the significance claim accordingly.

minor comments (6)

[§1.1] The phrase 'speech-based speech markers' should be simplified to 'speech markers' or 'speech-based markers'.
[§4.1] The section heading 'Performance Comparision' contains a typo; it should read 'Performance Comparison'.
[Table 2] The first row shows 'dishes"' with a stray quotation mark; please fix the formatting of the marker list.
[Appendix A, Algorithm 1] The loop 'for l from L−1 to 1' is not clearly matched to the layer indexing used elsewhere; clarify whether L is the number of layers or the last layer index, and align the initialization and termination conditions.
[§3.3] The phrase 'The number of samples per input was set to 10 (i.e., one original sample plus nine corrupted samples)' is inconsistent with the later mention of '10 corrupted samples per input'; please specify the exact corruption count.
[§4.2 and Appendix C] Tables 2 and 4 are referred to as being in the Appendix in the text, but Table 2 appears in the main body; update the cross-references.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: RSF's marker scores are model-derived, but the diagnostic gain is measured on held-out real data against SHAP/IG baselines using the same generation pipeline.

full rationale

RSF's derivation chain is not circular. The pipeline is: (1) fine-tune an LLM on AD/NC speech transcripts; (2) identify MPNs by causal tracing, perturbing known AD markers and measuring indirect effects; (3) backtrack from MPNs to input tokens via Algorithm 1 to obtain MPT/MPM scores; (4) generate synthetic transcripts with GPT-4o conditioned on the top-scoring markers; (5) fine-tune a new diagnostic model on original plus generated training transcripts; (6) evaluate on a held-out real test set. The final performance comparison (Table 1) is self-contained and externally anchored: the test set contains only real Pitt Corpus samples, and the SHAP/IG baselines use the identical generation and training protocol, differing only in marker source. The 3.5%/3.2% accuracy/F1 gain is therefore not forced by construction; it could have gone the other way. The marker scores in Algorithm 1 are deterministic functions of the trained model's weights and the measured indirect effects, but they are not fitted to the held-out labels or test set, and the 'most probable marker' claim is a model-interpretation claim rather than an independent prediction. Concerns about the validity of the linear backtracking for transformers (attention softmax nonlinearities, layer norms, residual stream) are substantive correctness risks, but they are not circularity: no equation reduces to its own input and no fitted parameter is renamed as a prediction. The paper does cite the same group's prior work (Mo et al. 2024, 2025; Li et al. 2025) for the data-generation prompt and framing, but the load-bearing evaluation is independent of those citations.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The central claim rests on several unverified modeling assumptions and free parameters, most notably the arbitrary causal tracing hyperparameters, the unspecified MPN selection, and the linear backtracking rule. The markers are internal to the model, and the data generation depends on a closed commercial API.

free parameters (7)

number of corrupted samples per input = 10
Section 3.3: causal tracing uses one original plus nine corrupted samples; arbitrary choice.
noise variance for causal tracing corruption = 1
Section 3.3: Gaussian noise variance set to 1; no sensitivity analysis.
maximum sequence length = 512
Section 3.3: tokens exceeding 512 truncated; affects which tokens are considered.
MPN selection threshold = not specified
The paper never states how many or which hidden states are selected as MPNs; a critical free parameter.
GPT-4o sampling temperature = 1
Section 3.2/3.4: fixed temperature 1 for marker extraction and data generation; non-deterministic.
K-means cluster count = not specified
Appendix C: K-means applied to group similar markers, but k is not given.
training hyperparameters = lr=1e-4, wd=0.01, epochs=20, batch=16
Section 3.6: chosen from a small grid; affects models but all configurations share them.

assumptions (4)

domain assumption Speech markers with highest probability of predicting AD must have highest probability of activating neurons with highest probability of predicting AD.
Section 2.1.1: stated as an observation, unproven; the entire marker selection rests on this equivalence.
domain assumption Corrupting known AD markers and measuring restoration of hidden states identifies causal neurons for AD prediction.
Section 2.1.1: adaptation of ROME causal tracing to AD classification; assumes perturbations isolate causal circuitry.
ad hoc to paper Backtracking can be performed by a linear weighted sum of indirect effects through model weights.
Algorithm 1: token scores are computed as a linear propagation ignoring attention interactions and nonlinearities; no theoretical justification.
domain assumption GPT-4o can generate realistic AD-like speech transcripts from marker prompts.
Section 3.4: relies on proprietary LLM for data generation; quality and diversity of synthetic data are not verified.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Reverse-Speech-Finder: A Neural Network Backtracking Architecture for Generating Alzheimer's Disease Speech Samples and Improving Diagnosis Performance." pith.science (2026). https://pith.science/paper/DSTMTW5Q

@misc{pith2026250517477,
  author       = {Pith},
  title        = {Pith review of: Reverse-Speech-Finder: A Neural Network Backtracking Architecture for Generating Alzheimer's Disease Speech Samples and Improving Diagnosis Performance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DSTMTW5Q}},
  note         = {Machine review of arXiv:2505.17477}
}

read the original abstract

This study introduces Reverse-Speech-Finder (RSF), a groundbreaking neural network backtracking architecture designed to enhance Alzheimer's Disease (AD) diagnosis through speech analysis. Leveraging the power of pre-trained large language models, RSF identifies and utilizes the most probable AD-specific speech markers, addressing both the scarcity of real AD speech samples and the challenge of limited interpretability in existing models. RSF's unique approach consists of three core innovations: Firstly, it exploits the observation that speech markers most probable of predicting AD, defined as the most probable speech-markers (MPMs), must have the highest probability of activating those neurons (in the neural network) with the highest probability of predicting AD, defined as the most probable neurons (MPNs). Secondly, it utilizes a speech token representation at the input layer, allowing backtracking from MPNs to identify the most probable speech-tokens (MPTs) of AD. Lastly, it develops an innovative backtracking method to track backwards from the MPNs to the input layer, identifying the MPTs and the corresponding MPMs, and ingeniously uncovering novel speech markers for AD detection. Experimental results demonstrate RSF's superiority over traditional methods such as SHAP and Integrated Gradients, achieving a 3.5% improvement in accuracy and a 3.2% boost in F1-score. By generating speech data that encapsulates novel markers, RSF not only mitigates the limitations of real data scarcity but also significantly enhances the robustness and accuracy of AD diagnostic models. These findings underscore RSF's potential as a transformative tool in speech-based AD detection, offering new insights into AD-related linguistic deficits and paving the way for more effective non-invasive early intervention strategies.

Figures

Figures reproduced from arXiv: 2505.17477 by the authors.

**Figure 1.** Overview of Reverse-Speech-Finder (RSF) of AD speech decline and hold significant potential for application in different disease contexts, offering a versatile framework to uncover the most probable factors in a wide range of classification tasks beyond medical diagnostics. 2 Research Methodology RSF proceeds in three stages (see [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 28 canonical work pages

[1]

Asir Abrar, Aetesam Ali khan Ashar, and Jiangjiang Liu. 2024. A survey on early-stage dementia detection using natural language processing: Datasets and approaches. In Proceedings of the 2024 8th International Conference on Information System and Data Mining, pages 21--27

work page 2024
[2]

Alzheimer's Association . 2024. Alzheimer's disease facts and figures. https://www.alz.org/alzheimers-dementia/facts-figures

work page 2024
[3]

Aparna Balagopalan, Benjamin Eyre, Frank Rudzicz, and Jekaterina Novikova. 2020. To BERT or not to BERT : comparing speech and language-based approaches for Alzheimer's disease detection. arXiv preprint arXiv:2008.01551

arXiv 2020
[4]

James T Becker, Fran c ois Boiler, Oscar L Lopez, Judith Saxton, and Karen L McGonigle. 1994. The natural history of Alzheimer's disease: description of study cohort and accuracy of diagnosis. Archives of Neurology, 51(6):585--594

work page 1994
[5]

Herv \'e Bredin. 2023. pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe. In 24th INTERSPEECH Conference (INTERSPEECH 2023), pages 1983--1987. ISCA

work page 2023
[6]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171--4186

work page 2019
[7]

Kewen Ding, Madhu Chetty, Azadeh Noori Hoshyar, Tanusri Bhattacharya, and Britt Klein. 2024. Speech based detection of Alzheimer's disease: a survey of AI techniques, datasets and challenges. Artificial Intelligence Review, 57(12):1--43

work page 2024
[8]

Ahmed Elazab, Changmiao Wang, Mohammed Abdelaziz, Jian Zhang, Jason Gu, Juan M Gorriz, Yudong Zhang, and Chunqi Chang. 2024. Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions. Expert Systems with Applications, page 124780

work page 2024

Show all 34 references

[9]

Elif Eyigoz, Sachin Mathur, Mar Santamaria, Guillermo Cecchi, and Melissa Naylor. 2020. Linguistic markers predict onset of Alzheimer's disease. EClinicalMedicine, 28

2020
[10]

Kathleen C Fraser, Jed A Meltzer, and Frank Rudzicz. 2015. Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer's Disease, 49(2):407--422

2015
[11]

Elaine Giles, Karalyn Patterson, and John R Hodges. 1996. Performance on the Boston Cookie Theft picture description task in patients with early dementia of the Alzheimer's type: missing information. Aphasiology, 10(4):395--408

1996
[12]

Jonathan Heitz, Gerold Schneider, and Nicolas Langer. 2024. https://arxiv.org/abs/2412.15772 Linguistic features extracted by GPT-4 improve Alzheimer's disease detection based on spontaneous speech . Preprint, arXiv:2412.15772

2024 arXiv
[13]

Loukas Ilias, Dimitris Askounis, and John Psarras. 2023. Detecting dementia from speech and transcripts using transformers. Computer Speech & Language, 79:101485

2023
[14]

Alyssa M Lanzi, Anna K Saylor, Davida Fromm, Houjun Liu, Brian MacWhinney, and Matthew L Cohen. 2023. DementiaBank : Theoretical rationale, protocol, and illustrative analyses. American Journal of Speech-Language Pathology, 32(2):426--438

2023
[15]

Liz Yuanxi Lee, Delshad Vaghari, Michael C Burkhart, Peter Tino, Marcella Montagnese, Zhuoyu Li, Katharina Z \"u hlsdorff, Joseph Giorgio, Guy Williams, Eddie Chong, et al. 2024. Robust and interpretable AI -guided marker for early dementia prediction in real-world clinical se...

2024
[16]

Victor O. K. Li, Yang Han, and Jacqueline C. K. Lam. 2025. https://arxiv.org/abs/2502.03938 Unravelling causal genetic biomarkers of Alzheimer's disease via neuron to gene-token backtracking in neural architecture: A groundbreaking reverse-gene-finder approach . Preprint, arXi...

2025 arXiv
[17]

Victor O. K. Li, Jacqueline C. K. Lam, Yang Han, Lawrence Y. L. Cheung, Jocelyn Downey, Tushar Kaistha, and Illana Gozes. 2021. Designing a protocol adopting an artificial intelligence ( AI )--driven approach for early diagnosis of late-onset Alzheimer's disease. Journal of Mo...

2021
[18]

Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30

2017
[19]

Saturnino Luz, Sofia De La Fuente Garcia, Fasih Haider, Davida Fromm, Brian MacWhinney, Alyssa Lanzi, Ya-Ning Chang, Chia-Ju Chou, and Yi-Chien Liu. 2024. Connected speech-based cognitive assessment in Chinese and English . arXiv preprint arXiv:2406.10272

2024 arXiv
[20]

Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in GPT . Advances in Neural Information Processing Systems, 35:17359--17372

2022
[21]

Tingyu Mo, Jacqueline C. K. Lam, Victor O. K. Li, and Lawrence Y. L. Cheung. 2024. https://arxiv.org/abs/2024.08.22.24312463 Leveraging large language models for identifying interpretable linguistic markers and enhancing Alzheimer's disease diagnostics . Preprint, medRxiv:2024...

2024
[22]

Tingyu Mo, Jacqueline C. K. Lam, Victor O. K. Li, and Lawrence Y. L. Cheung. 2025. https://arxiv.org/abs/2502.04394 DECT : Harnessing LLM -assisted fine-grained linguistic knowledge and label-switched and label-preserved data generation for diagnosis of Alzheimer's disease . P...

2025 arXiv
[23]

Kimberly D Mueller, Bruce Hermann, Jonilda Mecollari, and Lyn S Turkstra. 2018. Connected speech and language in mild cognitive impairment and Alzheimer’s disease: A review of picture description tasks. Journal of Clinical and Experimental Neuropsychology, 40(9):917--939

2018
[24]

OpenAI . 2024. GPT-4o API . https://platform.openai.com/docs/models/gpt-4o

2024
[25]

Ulla Petti, Simon Baker, and Anna Korhonen. 2020. A systematic literature review of automatic Alzheimer’s disease detection from speech and language. Journal of the American Medical Informatics Association, 27(11):1784--1797

2020
[26]

Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. 2023. Robust speech recognition via large-scale weak supervision. In International conference on machine learning, pages 28492--28518. PMLR

2023
[27]

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners

2019
[28]

Nils Reimers and Iryna Gurevych. 2019. https://arxiv.org/abs/1908.10084 Sentence-BERT : Sentence embeddings using siamese BERT -networks . In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics

2019 arXiv
[29]

Am \'e lie B Richard, Manon Lelandais, Karen T Reilly, and Sophie Jacquin-Courtois. 2024. Linguistic markers of subtle cognitive impairment in connected speech: A systematic review. Journal of Speech, Language, and Hearing Research, 67(12):4714--4733

2024
[30]

Louise Robinson, Eugene Tang, and John-Paul Taylor. 2015. Dementia: timely diagnosis and early intervention. BMJ, 350

2015
[31]

Ploypaphat Saltz, Shih Yin Lin, Sunny Chieh Cheng, and Dong Si. 2021. Dementia detection using transformer-based deep learning and natural language processing models. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pages 509--510. IEEE

2021
[32]

Mengke Shi, Gary Cheung, and Seyed Reza Shahamiri. 2023. Speech and language processing with deep learning for dementia diagnosis: A systematic review. Psychiatry Research, page 115538

2023
[33]

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In International Conference on Machine Learning, pages 3319--3328. PMLR

2017
[34]

Akshay Valsaraj, Ithihas Madala, Nikhil Garg, and Veeky Baths. 2021. Alzheimer's dementia detection using acoustic & linguistic features and pre-trained BERT . In 2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI), pages 171--175. IEEE

2021

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Asir Abrar, Aetesam Ali khan Ashar, and Jiangjiang Liu. 2024. A survey on early-stage dementia detection using natural language processing: Datasets and approaches. In Proceedings of the 2024 8th International Conference on Information System and Data Mining, pages 21--27

work page 2024

[2] [2]

Alzheimer's Association . 2024. Alzheimer's disease facts and figures. https://www.alz.org/alzheimers-dementia/facts-figures

work page 2024

[3] [3]

Aparna Balagopalan, Benjamin Eyre, Frank Rudzicz, and Jekaterina Novikova. 2020. To BERT or not to BERT : comparing speech and language-based approaches for Alzheimer's disease detection. arXiv preprint arXiv:2008.01551

arXiv 2020

[4] [4]

James T Becker, Fran c ois Boiler, Oscar L Lopez, Judith Saxton, and Karen L McGonigle. 1994. The natural history of Alzheimer's disease: description of study cohort and accuracy of diagnosis. Archives of Neurology, 51(6):585--594

work page 1994

[5] [5]

Herv \'e Bredin. 2023. pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe. In 24th INTERSPEECH Conference (INTERSPEECH 2023), pages 1983--1987. ISCA

work page 2023

[6] [6]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171--4186

work page 2019

[7] [7]

Kewen Ding, Madhu Chetty, Azadeh Noori Hoshyar, Tanusri Bhattacharya, and Britt Klein. 2024. Speech based detection of Alzheimer's disease: a survey of AI techniques, datasets and challenges. Artificial Intelligence Review, 57(12):1--43

work page 2024

[8] [8]

Ahmed Elazab, Changmiao Wang, Mohammed Abdelaziz, Jian Zhang, Jason Gu, Juan M Gorriz, Yudong Zhang, and Chunqi Chang. 2024. Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions. Expert Systems with Applications, page 124780

work page 2024