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
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“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
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
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- CoChat. (2026). Citation check: Interpreting Pretrained Speech Models for Automatic Speech Assessment of Voice Disorders. https://app.cochat.ai/papers/10.1007/978-3-031-67278-1_5
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- Lau, H. S., Huntly, M., Morgan, N., Iyenoma, A., Zeng, B., & Bashford, T. (2024). Interpreting Pretrained Speech Models for Automatic Speech Assessment of Voice Disorders. Lecture notes in computer science. https://doi.org/10.1007/978-3-031-67278-1_5
