The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Interpreting Pretrained Speech Models for Automatic Speech Assessment of Voice Disorders
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“Lastly, attention-based explanations were reported in 2 studies, where interpretability was derived either through direct visualization of attention weights [113] or through attention rollout-based aggregation of attention across layers [57].”
From Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review (2026) · cites this paper as [57]
Citations
Cites
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
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
Audio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
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
AST: Audio Spectrogram Transformer
Gong, Y., Chung, Y.-A., & Glass, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-698 · Cited by 1,159
Quantifying Attention Flow in Transformers
Abnar, S., & Zuidema, W. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.385 · Cited by 995
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers
Chefer, H., Gur, S., & Wolf, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00045 · Cited by 313
Machine learning approaches to identify Parkinson's disease using voice signal features
Alshammri, R., Alharbi, G. M., Alharbi, E., & Almubark, I. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1084001 · Cited by 182
AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark
Becker, S., Vielhaben, J., Ackermann, M., Müller, K., Lapuschkin, S., & Samek, W. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2023.11.038 · Cited by 181
Simple and Effective Zero-shot Cross-lingual Phoneme Recognition
Xu, Q., Baevski, A., & Auli, M. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-60 · Cited by 115
Costantini, G., Cesarini, V., Leo, P. D., Amato, F., Suppa, A., Asci, F., Pisani, A., Calculli, A., & Saggio, G. (2023) · Sensors · doi:10.3390/s23042293 · Cited by 111
Acoustic differences between healthy and depressed people: a cross-situation study
Wang, J., Zhang, L., Liu, T., Pan, W., Hu, B., & Zhu, T. (2019) · BMC Psychiatry · doi:10.1186/s12888-019-2300-7 · Cited by 98
Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data
Han, J., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Mascolo, C. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414576 · Cited by 85
Toward a realistic model of speech processing in the brain with self-supervised learningPreprint
Millet, J., Caucheteux, C., Orhan, P., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.01685 · Cited by 40
Evaluation of Acoustic Analyses of Voice in Nonoptimized Conditions
van der Woerd, B. D., Wu, M., Parsa, V., Doyle, P. C., & Fung, K. (2020) · Journal of Speech Language and Hearing Research · doi:10.1044/2020_jslhr-20-00212 · Cited by 38
Nathan, V., Vatanparvar, K., Rahman, M., Nemati, E., & Kuang, J. (2019) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2019.8771043 · Cited by 37
Huckvale, M. A., & Buciuleac, C. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1507 · Cited by 21
Chen, X., Zhu, Q., Zhang, J., & Dai, L.-R. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746205 · Cited by 18
Nallanthighal, V. S., Härmä, A., & Strik, H. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747785 · Cited by 18
XAI-based Comparison of Audio Event Classifiers with different Input Representations
Frommholz, A., Seipel, F., Lapuschkin, S., Samek, W., & Vielhaben, J. (2023) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3617233.3617265 · Cited by 13
Wagner, D., Baumann, I., Braun, F. M., et al. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-464 · Cited by 10
Zeng, B., Williams, E. M., Owen, C., Zhang, C., Davies, S. K., Evans, K., & Preudhomme, S.-R. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1167902 · Cited by 7
Self-citationPenney, J., Gibson, A., Cox, F., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-729 · Cited by 6
Liu, Z., Huckvale, M. A., & McGlashan, J. A. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10154 · Cited by 4
Research works printed but not linked: 4
- Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook (2017)
- Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70, Sydney (2017)
- Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) · arXiv
- van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Cited by
Sorted by citations
Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review
Ebraheem, M., Toghranegar, J., Bridge2AI-Voice Consortium, Bensoussan, Y., & Templeton, J. M. (2026) · Journal of Medical Internet Research · doi:10.2196/83790
Metadata
- Authors
- Lau, H. S., Huntly, M., Morgan, N., Iyenoma, A., Zeng, B., & Bashford, T.
- Affiliations
- University of Wales (GB)
- University of South Wales (GB)
- Corresponding author
- Lau, Hok Shing
- Venue
- Lecture notes in computer science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 59-72
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4401551438
- Cited by
- 2
- References
- 26
- Citations by year
- 2025: 2
- FWCI
- 5.99
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- University of Wales Trinity Saint David
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- voice disorder detection, Audio Spectrogram Transformer, attention rollout, model interpretability, relevance maps, fine-tuning
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
