Subspace-Based Learning for Automatic Dysarthric Speech Detection
Open accessAbstract
To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated with dysarthric speech, we propose to automatically discriminate between healthy and dysarthric speakers exploiting spectro-temporal subspaces of speech. Spectro-temporal subspaces are extracted using singular value decomposition, and dysarthric speech detection is achieved by applying a subspace-based discriminant analysis. Experimental results on databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed subspace-based approach using temporal subspaces is more advantageous than using spectral subspaces, also outperforming several state-of-the-art automatic dysarthric speech detection techniques.
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?
Conventional speech intelligibility assessment approaches often require the contents spoken by different speakers to be the same.
“This flexible design is in contrast to conventional speech intelligibility assessment approaches that often require the contents spoken by different speakers to be the same [96], [97], [90].”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [90]
Speaker embedding features were previously only studied for speech impairment assessment.
“Speaker embedding features were previously only studied for speech impairment assessment [88]-[90].”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [90]
Cites
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680 · see citing works
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417 · see citing works
Fisher discriminant analysis with kernels
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., & Mullers, K. R. (2003) · Machine learning for signal processing ... · doi:10.1109/nnsp.1999.788121 · Cited by 2,660 · see citing works
Differential diagnostic patterns of dysarthria
Darley, F. L., Aronson, A. E., & Brown, J. R. (1969) · Journal of Speech and Hearing Research · doi:10.1044/jshr.1202.246 · Cited by 1,323 · 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
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson’s Disease
Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2183367 · Cited by 732 · see citing works
Grassmann discriminant analysis
Hamm, J., & Lee, D. D. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390204 · Cited by 593 · see citing works
Dysarthric speech database for universal access research
Kim, H., Hasegawa-Johnson, M., Perlman, A., Gunderson, J., Huang, T. S., Watkin, K., & Frame, S. (2008) · Interspeech 2008 · doi:10.21437/interspeech.2008-480 · Cited by 397 · see citing works
Manifold-Manifold Distance with application to face recognition based on image set
Wang, R., Shan, S., Chen, X., & Gao, W. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587719 · Cited by 396 · see citing works
Subspace-based signal analysis using singular value decomposition
Van Der Veen, A.-J., Deprettere, E. F., & Swindlehurst, A. L. (1993) · Proceedings of the IEEE · doi:10.1109/5.237536 · Cited by 348 · see citing works
Acoustic Discrimination of Pathological Voice
Parsa, V., & Jamieson, D. G. (2001) · Journal of Speech Language and Hearing Research · doi:10.1044/1092-4388(2001/027 · Cited by 279 · see citing works
New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease
Orozco‐Arroyave, J. R., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Rátiva, M. C. G., & Nöth, E. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3c643i6hcfzo · Cited by 248 · see citing works
Automatic detection of Parkinson's disease in running speech spoken in three different languages
Orozco‐Arroyave, J. R., Hönig, F., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Daqrouq, K. O., Skodda, S., Rusz, J., & Nöth, E. (2016) · The Journal of the Acoustical Society of America · doi:10.1121/1.4939739 · Cited by 218 · see citing works
Parkinson disease prediction using intrinsic mode function based features from speech signal
Karan, B., Sahu, S. S., & Mahto, K. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2019.05.005 · Cited by 165 · see citing works
Automatic Evaluation of Articulatory Disorders in Parkinson’s Disease
Novotný, M., Rusz, J., Čmejla, R., & Růžička, E. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2329734 · Cited by 158 · see citing works
Detecting Parkinson’s disease from sustained phonation and speech signals
Vaičiukynas, E., Verikas, A., Gelžinis, A., & Bačauskienė, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185613 · Cited by 110 · see citing works
Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks
Berus, L., Klančnik, S., Brezočnik, M., & Ficko, M. (2018) · Sensors · doi:10.3390/s19010016 · Cited by 107 · see citing works
Rosen, K. M., Kent, R. D., Delaney, A. L., & Duffy, J. R. (2006) · Journal of Speech Language and Hearing Research · doi:10.1044/1092-4388(2006/031 · Cited by 99 · see citing works
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
Shaokang Chen, A., Sanderson, C., Harandi, M. T., & Lovell, B. C. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.65 · Cited by 79 · see citing works
Automatic evaluation of parkinson's speech — acoustic, prosodic and voice related cues
Bocklet, T., Steidl, S., Nöth, E., & Skodda, S. (2013) · Interspeech 2013 · doi:10.21437/interspeech.2013-313 · Cited by 66 · see citing works
Spectral and cepstral analyses for Parkinson's disease detection in Spanish vowels and words
Orozco‐Arroyave, J. R., Hönig, F., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., & Nöth, E. (2015) · Expert Systems · doi:10.1111/exsy.12106 · Cited by 60 · see citing works
Wang, J., Kothalkar, P. V., Cao, B., & Heitzman, D. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1542 · Cited by 60 · see citing works
Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis
Norel, R., Pietrowicz, M., Agurto, C., Rishoni, S., & Cecchi, G. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2389 · Cited by 60 · see citing works
Spectro-Temporal Sparsity Characterization for Dysarthric Speech Detection
Kodrasi, I., & Bourlard, H. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2020.2985066 · Cited by 52 · see citing works
Self-citationAutomatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders
Baghai-Ravary, L., & Beet, S. W. (2012) · Springer briefs in electrical and computer engineering · doi:10.1007/978-1-4614-4574-6 · Cited by 45 · see citing works
Automatic Detection of Parkinson’s Disease Based on Modulated Vowels
Hemmerling, D., Orozco‐Arroyave, J. R., Skalski, A., Gajda, J., & Nöth, E. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1062 · Cited by 45 · see citing works
Automatic system to detect the type of voice pathology
Jothilakshmi, S. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.03.036 · Cited by 43 · see citing works
Cross-Database Models for the Classification of Dysarthria Presence
Gillespie, S., Logan, Y.-Y., Moore, E., Laures-Gore, J. S., Russell, S., & Patel, R. M. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-216 · Cited by 42 · see citing works
Kacha, A., Grenez, F., Orozco‐Arroyave, J. R., & Schoentgen, J. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.07.001 · Cited by 35 · see citing works
Illa, A., Patel, D., Yamini, B., ss, M., Shivashankar, N., Preethish‐Kumar, V., vengalii, S., Polavarapui, K., Nashi, S., Nalini, A., et al. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461836 · Cited by 25 · see citing works
Kodrasi, I., & Bourlard, H. A. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683107 · Cited by 22 · see citing works
Self-citationAcoustic Analysis and Non Linear Dynamics Applied to Voice Pathology Detection: A Review
Orozco‐Arroyave, J. R., Vargas‐Bonilla, J. F., & Delgado‐Trejos, E. (2012) · Recent Patents on Signal Processing · doi:10.2174/2210686311202020096 · Cited by 21 · see citing works
Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease
López-Pabón, F. O., Arias‐Vergara, T., & Orozco‐Arroyave, J. R. (2020) · TecnoLógicas · doi:10.22430/22565337.1401 · Cited by 20 · see citing works
Spectral Subspace Analysis for Automatic Assessment of Pathological Speech Intelligibility
Janbakhshi, P., Kodrasi, I., & Bourlard, H. A. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2791 · Cited by 18 · see citing works
Self-citationA Riemannian gossip approach to subspace learning on Grassmann manifold
Mishra, B., Kasai, H., Jawanpuria, P., & Saroop, A. (2019) · Machine Learning · doi:10.1007/s10994-018-05775-x · Cited by 17 · see citing works
Automatic Pathological Speech Intelligibility Assessment Exploiting Subspace-Based Analyses
Janbakhshi, P., Kodrasi, I., & Bourlard, H. A. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2997197 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Chen, F., Yang, C., & Khishe, M. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103688 · Cited by 75 · see citing works
Zhang, J., Wu, J., Qiu, Y., Song, A., Li, W., Li, X., & Liu, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106517 · Cited by 69 · see citing works
Geng, M., Xie, X., Ye, Z., Wang, T., Li, G., Hu, S., Liu, X., & Meng, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3195113 · Cited by 48 · see citing works
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition
Jin, Z., Geng, M., Deng, J., Wang, T., Hu, S., Li, G., & Liu, X. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3323888 · Cited by 46 · see citing works
Schu, G. G., Janbakhshi, P., & Kodrasi, I. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095981 · Cited by 41 · see citing works
Self-citationBrahmi, Z., Mahyoob, M., Al-Sarem, M., Algaraady, J., Bousselmi, K., & Alblwi, A. (2024) · Psychology Research and Behavior Management · doi:10.2147/prbm.s460283 · Cited by 36 · see citing works
Temporal Envelope and Fine Structure Cues for Dysarthric Speech Detection Using CNNs
Kodrasi, I. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3108509 · Cited by 33 · see citing works
Self-citationYao, D., Chi, W., & Khishe, M. (2022) · Applied Acoustics · doi:10.1016/j.apacoust.2022.109003 · Cited by 28 · see citing works
Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones
Pillai, A., Nepal, S. K., Wang, W., Nemesure, M. D., Heinz, M. V., Price, G. D., Lekkas, D., Collins, A. C., Griffin, T. Z., Buck, B., et al. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631452 · Cited by 26 · see citing works
Speech Technology for Automatic Recognition and Assessment of Dysarthric Speech: An Overview
Bhat, C. G., & Strik, H. (2025) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-23-00740 · Cited by 21 · see citing works
Janbakhshi, P., & Kodrasi, I. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747205 · Cited by 16 · see citing works
Self-citationSpectro-Temporal Deep Features for Disordered Speech Assessment and Recognition
Geng, M., Liu, S., Yu, J., Xie, X., Hu, S., Ye, Z., Jin, Z., Liu, X., & Meng, H. M. L. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-60 · Cited by 16 · see citing works
Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s
Fougeron, C., Kodrasi, I., & Laganaro, M. (2022) · Brain Sciences · doi:10.3390/brainsci12111471 · Cited by 10 · see citing works
Self-citationSheikh, S. A., Sahidullah, M., & Kodrasi, I. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3591062 · Cited by 11 · see citing works
Self-citationA comprehensive survey of automatic dysarthric speech recognition
Yadav, S., Yadav, D. M., & Desai, K. R. (2023) · International Journal of Informatics and Communication Technology (IJ-ICT) · doi:10.11591/ijict.v12i3.pp242-250 · Cited by 5 · see citing works
Yadav, S., & Yadav, D. M. (2024) · International Conference on Electronics, Communication and Signal Processing (ICECSP) · doi:10.1109/icecsp61809.2024.10698732 · Cited by 3 · see citing works
Dysarthric Speech Recognition Using Deep Convolution Neural Network
Kure, N., & Dhonde, S. B. (2024) · IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) · doi:10.1109/iciteics61368.2024.10625297 · Cited by 3 · see citing works
Denoising Convolutional Autoencoder Based Approach for Disordered Speech Recognition
Chandrakala, S., & Veni, S. V. (2023) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213023500586 · Cited by 2 · see citing works
Suppressing Noise Disparity in Training data for Automatic Pathological Speech Detection
Amiri, M., & Kodrasi, I. (2024) · International Workshop on Acoustic Signal Enhancement · doi:10.1109/iwaenc61483.2024.10694597 · Cited by 2 · see citing works
Self-citationGeng, M., Xie, X., Deng, J., Jin, Z., Li, G., Wang, T., Hu, S., Li, Z., Meng, H. M. L., & Liu, X. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3547217 · Cited by 2 · see citing works
Dysarthric Speech Data Augmentation Using Generative Adversarial Networks
Yadav, S., & Yadav, D. M. (2025) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect63952.2025.10958874 · Cited by 1 · see citing works
Efficacy of Current Dysarthric Speech Recognition Techniques
Malik, M., & Khanam, R. (2023) · doi:10.1109/icacctech61146.2023.00111 · Cited by 1 · see citing works
SARNet: Speaker-Attentive ResNet for Quantification of Dysarthria Severity
Yin, H., Zhang, X., Yu, Y., & Yu, N. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10662779 · Cited by 1 · see citing works
Zhang, J., Qiu, Y., Liu, Y., Yi, X., Yang, J., Yang, X., Ma, M., & Song, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110940 · Cited by 1 · see citing works
A Comprehensive Survey of Automatic Dysarthric Speech Recognition
Pophale, C. D., & Chavan, S. D. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i9s.7392 · Cited by 1 · see citing works
2021 Index IEEE Signal Processing Letters Vol. 28
(2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3145253
Survey of Automatic Dysarthric Speech Recognition
Kure, E. A. N. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i10.8622
Dysarthria Voice Enhancement Using Novel WPT-TDO Algorithm
Kotangale, K. B., Parkale, Y. V., & Patil, V. N. (2024) · International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) · doi:10.1109/icetems64039.2024.10964968
Dysarthric Speech Data Augmentation using Generative Adversarial Networks
Kure, N., & Dhonde, S. B. (2025) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet64471.2025.11140902
Approaches for Controlled / Dysarthric Speech Classification Based on Sinusoidal Model
T, S. K., & Jayan, A. R. (2025) · International Conference on Advancements in Power, Communication and Intelligent Systems (APCI) · doi:10.1109/apci65531.2025.11137157
Kotangale, K. B., & Parkale, Y. V. (2025) · Circuits Systems and Signal Processing · doi:10.1007/s00034-025-03400-6
Zaheera, F., & Pradhan, G. (2026) · Speech Communication · doi:10.1016/j.specom.2026.103486
- Authors
- Janbakhshi, P., Kodrasi, I., & Bourlard, H.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- IEEE Signal Processing Letters
- Published
- 2021
- Volume
- 28
- Pages
- 96-100
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W3110715635
- Cited by
- 32
- References
- 36
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 6 · 2023: 9 · 2022: 5 · 2021: 3
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- IEEE Signal Processing Letters (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (CRSII5_173711)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- dysarthria, speech-language pathology, singular value decomposition, speech spectrograms
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
