Spectro-Temporal Sparsity Characterization for Dysarthric Speech Detection
Open accessAbstract
To assist the clinical diagnosis and treatment of neurological diseases that cause speech dysarthria such as Parkinson's disease (PD), it is of paramount importance to craft robust features which can be used to automatically discriminate between healthy and dysarthric speech. Since dysarthric speech of patients suffering from PD is breathy, semi-whispery, and is characterized by abnormal pauses and imprecise articulation, it can be expected that its spectro-temporal sparsity differs from the spectro-temporal sparsity of healthy speech. While we have recently successfully used temporal sparsity characterization for dysarthric speech detection, characterizing spectral sparsity poses the challenge of constructing a valid feature vector from signals with a different number of unaligned time frames. Further, although several non-parametric and parametric measures of sparsity exist, it is unknown which sparsity measure yields the best performance in the context of dysarthric speech detection. The objective of this paper is to demonstrate the advantages of spectro-temporal sparsity characterization for automatic dysarthric speech detection. To this end, we first provide a numerical analysis of the suitability of different non-parametric and parametric measures (i.e., l1-norm, kurtosis, Shannon entropy, Gini index, shape parameter of a Chi distribution, and shape parameter of a Weibull distribution) for sparsity characterization. It is shown that kurtosis, the Gini index, and the parametric sparsity measures are advantageous sparsity measures, whereas the l1-norm and entropy measures fail to robustly characterize the temporal sparsity of signals with a different number of time frames. Second, we propose to characterize the spectral sparsity of an utterance by initially time-aligning it to the same utterance uttered by a (arbitrarily selected) reference speaker using dynamic time warping. Experimental results on a Spanish database of healthy and dysarthric speech show that estimating the spectro-temporal sparsity using the Gini index or the parametric sparsity measures and using it as a feature in a support vector machine results in a high classification accuracy of 83.3%.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Sources of variability commonly found in normal speech, including accent or gender, when compounded with variability over age and speech pathology severity, create large diversity among dysarthric and elderly speakers.
“Sources of variability commonly found in normal speech including accent or gender, when further compounded with those over age and speech pathology severity, create large diversity among dysarthric and elderly speakers [39], [51].”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [39]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526 · see citing works
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 11,966 · see citing works
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680 · see citing works
An Introduction to Numerical Analysis
S., F., & ATKINSON, K. E. (1990) · Mathematics of Computation · doi:10.2307/2008519 · Cited by 3,093 · see citing works
Sparse Approximate Solutions to Linear Systems
Natarajan, B. K. (1995) · SIAM Journal on Computing · doi:10.1137/s0097539792240406 · Cited by 2,866 · see citing works
A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
Friedman, M. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731944 · Cited by 2,785 · cited 1× · see citing works
“Consequently, usual statistical tests for multiple comparisons such as ANOVA [37] or the Friedman test [38] are inappropriate [39], [40].”
MethodThe Measurement of the Inequality of Incomes
Dalton, H. (1920) · The Economic Journal · doi:10.2307/2223525 · Cited by 1,488 · 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
Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's DiseasePreprint
Little, M. A., McSharry, P., Hunter, E. J., Spielman, J. L., & Ramig, L. O. (2008) · Nature Precedings · doi:10.1109/tbme.2008.2005954 · Cited by 1,194 · see citing works
Inference for the Generalization Error
Nadeau, C., & Bengio, Y. (2003) · Machine Learning · doi:10.1023/a:1024068626366 · Cited by 1,165 · see citing works
Prevalence of Parkinson’s disease across North America
Marras, C., Beck, J. C., Bower, J. H., Roberts, E. M., Ritz, B. R., Ross, G. W., Abbott, R. D., Savica, R., Eeden, S. K. V. D., Willis, A. W., et al. (2018) · npj Parkinson s Disease · doi:10.1038/s41531-018-0058-0 · Cited by 972 · see citing works
Comparing Measures of Sparsity
Hurley, N., & Rickard, S. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2027527 · Cited by 772 · see citing works
An introduction to numerical analysis
(1990) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(90)90206-x · Cited by 755 · 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
An affine scaling methodology for best basis selection
Rao, B. D., & Kreutz-Delgado, K. (1999) · IEEE Transactions on Signal Processing · doi:10.1109/78.738251 · Cited by 522 · see citing works
Image Inpainting by Patch Propagation Using Patch Sparsity
Xu, Z., & Sun, J. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2042098 · Cited by 518 · see citing works
Formant Centralization Ratio: A Proposal for a New Acoustic Measure of Dysarthric Speech
Sapir, S., Ramig, L. O., Spielman, J. L., & Fox, C. (2009) · Journal of Speech Language and Hearing Research · doi:10.1044/1092-4388(2009/08-0184 · Cited by 350 · see citing works
Feature Selection Based on Structured Sparsity: A Comprehensive Study
Gui, J., Sun, Z., Ji, S., Tao, D., & Tan, T. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2551724 · Cited by 335 · see citing works
Vowel Articulation in Parkinson's Disease
Skodda, S., Visser, W., & Schlegel, U. S. (2010) · Journal of Voice · doi:10.1016/j.jvoice.2010.01.009 · Cited by 274 · see citing works
Rusz, J., Čmejla, R., Tykalová, T., Růžičková, H., Klempíř, J., Majerová, V., Picmausová, J., Roth, J., & Růžička, E. (2013) · The Journal of the Acoustical Society of America · doi:10.1121/1.4816541 · Cited by 264 · 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
Sparse Representations in Audio and Music: From Coding to Source Separation
Plumbley, M. D., Blumensath, T., Daudet, L., Gribonval, R., & Davies, M. E. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2030345 · Cited by 207 · see citing works
Speech enhancement using MMSE short time spectral estimation with gamma distributed speech priors
Martin, R. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5743702 · Cited by 203 · see citing works
Factor structure of the unified Parkinson's disease rating scale: Motor examination section
Stebbins, G. T., & Goetz, C. G. (1998) · Movement Disorders · doi:10.1002/mds.870130404 · Cited by 201 · 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
Statistical Methods and Scientific Inference.
Pyke, R., Fisher, R. A., Siegel, S., & Fraser, D. A. S. (1957) · Journal of the American Statistical Association · doi:10.2307/2280908 · Cited by 143 · see citing works
Dealing with the evaluation of supervised classification algorithms
Santafé, G., Inza, I., & Lozano, J. A. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9433-y · Cited by 139 · see citing works
Moro-Velázquez, L., Gómez-García, J. A., Godino-Llorente, J. I., Villalba, J., Orozco‐Arroyave, J. R., & Dehak, N. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.11.001 · Cited by 92 · see citing works
Shrinkage mappings and their induced penalty functions
Chartrand, R. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853752 · Cited by 84 · 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
Voiced/unvoiced transitions in speech as a potential bio-marker to detect Parkinson's disease
Orozco‐Arroyave, J. R., Hönig, F., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Skodda, S., Rusz, J., & Nöth, E. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-34 · Cited by 55 · 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
Sparsity-Induced Similarity Measure and Its Applications
Cheng, H., Liu, Z., Hou, L., & Yang, J. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2225911 · Cited by 42 · see citing works
Motor Speech Disorders: Clues to Neurologic Diagnosis
Duffy, J. R. (2000) · Humana Press eBooks · doi:10.1007/978-1-59259-410-8_2 · Cited by 40 · see citing works
Segment-dependent dynamics in predicting Parkinson's disease
Williamson, J. R., Quatieri, T. F., Helfer, B. S., Perricone, J., Ghosh, S., Ciccarelli, G. A., & Mehta, D. D. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-187 · Cited by 36 · see citing works
Statistical Modeling of the Speech Signal
Tashev, I., & Acero, A. (2010) · Cited by 28 · see citing works
Adler, C. H., & Ahlskog, J. E. (2000) · Humana Press eBooks · Cited by 26 · see citing works
Sztahó, D., Kiss, G. J., & Vicsi, K. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-183 · Cited by 23 · see citing works
Log-spectral magnitude MMSE estimators under super-Gaussian densities
Hendriks, R. C., Heusdens, R. R., & Jensen, J. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-411 · Cited by 23 · 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-citationFang, C., Li, H., Ma, L., & Zhang, M. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/2431573 · Cited by 14 · see citing works
Motor Speech Disorders: Clues to Neurologic Diagnosis
Duffy, J. R. (2003) · Parkinson's Disease and Movement Disorders · doi:10.1385/1-59259-410-7:35 · Cited by 14 · see citing works
Signal-Dependent Penalty Functions for Robust Acoustic Multi-Channel Equalization
Kodrasi, I., & Doclo, S. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2699326 · Cited by 8 · see citing works
Self-citationGeneralized kurtosis and applications in blind equalization of MIMO channels
Tkacenko, A., & Vaidyanathan, P. P. (2001) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2001.987023 · Cited by 7 · see citing works
Statistical Modeling of Speech Spectral Coefficients in Patients with Parkinson's Disease.
Kodrasi, I., & Bourlard, H. A. (2018) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
1 of the 46 references couldn’t be matched to a known paper.
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
Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey
Amato, F., Saggio, G., Cesarini, V., Olmo, G., & Costantini, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119651 · Cited by 52 · 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-citationSubspace-Based Learning for Automatic Dysarthric Speech Detection
Janbakhshi, P., Kodrasi, I., & Bourlard, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3044503 · Cited by 32 · see citing works
Self-citationZhang, T., Lin, L., & Xue, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119650 · Cited by 32 · see citing works
Skibińska, J., & Hošek, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21175 · Cited by 28 · see citing works
Yao, D., Chi, W., & Khishe, M. (2022) · Applied Acoustics · doi:10.1016/j.apacoust.2022.109003 · Cited by 28 · see citing works
Janbakhshi, P., Kodrasi, I., & Bourlard, H. A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413922 · Cited by 21 · see citing works
Self-citationJanbakhshi, 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-citationKodrasi, I., Pernon, M., Laganaro, M., & Bourlard, H. A. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2253 · Cited by 16 · see citing works
Self-citationHistogram of States Based Assistive System for Speech Impairment Due to Neurological Disorders
Chandrakala, S., Malini, S., & Veni, S. V. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3125314 · Cited by 15 · see citing works
Kodrasi, I., Pernon, M., Laganaro, M., & Bourlard, H. A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414283 · Cited by 12 · see citing works
Self-citationGraph Neural Networks for Parkinson’s Disease Detection
Sheikh, S. A., Kaloga, Y., Sahidullah, M., & Kodrasi, I. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890110 · Cited by 11 · see citing works
Self-citationSupervised Speech Representation Learning for Parkinson's Disease ClassificationPreprint
Janbakhshi, P., & Kodrasi, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00531 · Cited by 11 · see citing works
Self-citationDifferentiation 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-citationVoice feature description of Parkinson’s disease based on co-occurrence direction attribute topology
Zhang, T., Lin, L., Tian, J., Xue, Z., & Guo, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106097 · Cited by 9 · see citing works
Sheikh, 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-citationWang, Y., Li, F., Zhang, X., Wang, P., Li, Y., & Zhang, Y. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02944-6 · Cited by 7 · see citing works
A 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
Jegan, R., & Jayagowri, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91916-6.00007-2 · 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
Variational Autoencoder for Personalized Pathological Speech Enhancement
Hou, M., & Kodrasi, I. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226500 · Cited by 1 · see citing works
Self-citationLeung, W.-Z., Christensen, H., & Goetze, S. (2026) · Speech Communication · doi:10.1016/j.specom.2026.103379 · Cited by 1 · see citing works
Speech-Based Parkinson's Disease Detection Using MFCC Features and Deep Representation Learning
Hossain, R., Banu, A., Arefin, M. S., & Hussain, M. M. M. (2026) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl67539.2026.11451863 · Cited by 1 · see citing works
Analysis and Classification of Dysarthric Speech
Tyagi, V., Dev, A., & Bansal, P. (2023) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda60357.2023.10482956 · Cited by 1 · see citing works
Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection
Kaloga, Y., Sheikh, S. A., & Kodrasi, I. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888902 · Cited by 1 · see citing works
Self-citationReszka, J., Janbakhshi, P., Purohit, T., & Mohammadi, S. (2025) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw65056.2025.11011023 · 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
Kodrasi, I., Pernon, M., Laganaro, M., & Bourlard, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07542 · Cited by 1 · see citing works
Self-citationJanbakhshi, P., Kodrasi, I., & Bourlard, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07545
Self-citationFeng, Y. (2021) · The Journal of Engineering · doi:10.1049/tje2.12066
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
Veni, S. V., & Santhi, B. (2024) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213024500143
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
Silva, L., Floriano, A., Valadão, C., Caldeira, E. M. D. O., Krishnan, S., & Bastos-Filho, T. (2026) · Frontiers in Signal Processing · doi:10.3389/frsip.2025.1700044
Development of a Mobile Application for Disordered Polish Speech Recording
Babiński, M., Bączyk, A., Wesołowski, J., & Piernicki, T. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-21376-1_16
Advances in Speech Enhancement: A Comprehensive Review of Noise Suppression Techniques
Tanwar, P. S., Somkuwar, A. K., & Gumasta, R. K. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7090466
Hybrid MFCC–HHT modeling for dysarthria and severity-level classification
Thirumuru, R., & Akkiraju, B. (2026) · International Journal of Speech Technology · doi:10.1007/s10772-026-10288-3
Zaheera, F., & Pradhan, G. (2026) · Speech Communication · doi:10.1016/j.specom.2026.103486
- Authors
- Kodrasi, I., & Bourlard, H.
- Affiliations
- Idiap Research Institute (CH)
- ORCID
- Kodrasi, Ina
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2020
- Volume
- 28
- Pages
- 1210-1222
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W3015342653
- Cited by
- 52
- References
- 46
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 6 · 2023: 13 · 2022: 6 · 2021: 6 · 2020: 4
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- IEEE/ACM Transactions on Audio Speech and Language Processing (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (CRSII5_173711)
- MoSpeeDi
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- Parkinson's disease, spectral sparsity, temporal sparsity, sparsity measures, kurtosis, Gini coefficient, Weibull distribution, chi distribution, dynamic time warping, temporal alignment, speech features, support vector machine classification
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
