{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:18:32Z","timestamp":1770329912342,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00034-024-02770-7","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T08:02:55Z","timestamp":1722499375000},"page":"7044-7063","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dysarthric Severity Categorization Based on Speech Intelligibility: A Hybrid Approach"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8043-7493","authenticated-orcid":false,"given":"Vidya","family":"M.","sequence":"first","affiliation":[]},{"given":"Ganesh Vaidyanathan","family":"S.","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"2770_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3223444","author":"ZK Abdul","year":"2022","unstructured":"Z.K. Abdul, A.K. Al-Talabani, Mel frequency cepstral coefficient and its applications: a review. IEEE Access (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3223444","journal-title":"IEEE Access"},{"key":"2770_CR2","doi-asserted-by":"publisher","first-page":"18183","DOI":"10.1109\/ACCESS.2021.3053335","volume":"9","author":"BA Al-Qatab","year":"2021","unstructured":"B.A. Al-Qatab, M.B. Mustafa, Classification of dysarthric speech according to the severity of impairment: an analysis of acoustic features. IEEE Access 9, 18183\u201318194 (2021)","journal-title":"IEEE Access"},{"issue":"1","key":"2770_CR3","doi-asserted-by":"publisher","first-page":"384","DOI":"10.2174\/1874944501912010384","volume":"12","author":"AM Altaher","year":"2019","unstructured":"A.M. Altaher, S.Y. Chu, R.A. Razak, A report of assessment tools for individuals with dysarthria. Open Public Health J. 12(1), 384 (2019)","journal-title":"Open Public Health J."},{"key":"2770_CR4","unstructured":"Ng. Andrew, Machine learning yearning URL: http:\/\/www.mlyearning.org\/(96) 139 (2017)"},{"key":"2770_CR5","doi-asserted-by":"publisher","first-page":"101137","DOI":"10.1016\/j.ecoinf.2020.101137","volume":"60","author":"DJ Benkendorf","year":"2020","unstructured":"D.J. Benkendorf, C.P. Hawkins, Effects of sample size and network depth on a deep learning approach to species distribution modeling. Eco. Inform. 60, 101137 (2020)","journal-title":"Eco. Inform."},{"issue":"2","key":"2770_CR6","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/JSTSP.2020.2967652","volume":"14","author":"C Bhat","year":"2020","unstructured":"C. Bhat, H. Strik, Automatic assessment of sentence-level dysarthria intelligibility using BLSTM. IEEE J. Sel. Top. Sig. Process. 14(2), 322\u2013330 (2020)","journal-title":"IEEE J. Sel. Top. Sig. Process."},{"key":"2770_CR7","doi-asserted-by":"crossref","unstructured":"C. Bhat, B. Vachhani, S.K. Kopparapu, Automatic assessment of dysarthria severity level using audio descriptors. In IEEE International conference on acoustics, speech and signal processing (ICASSP): 5070\u20135074 (2017)","DOI":"10.1109\/ICASSP.2017.7953122"},{"key":"2770_CR8","volume-title":"Bradley's Neurology in Clinical Practice e-Book","author":"RB Daroff","year":"2015","unstructured":"R.B. Daroff, J. Jankovic, J.C. Mazziotta, S.L. Pomeroy, Bradley\u2019s Neurology in Clinical Practice e-Book (Elsevier, 2015)"},{"issue":"4","key":"2770_CR9","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","volume":"28","author":"S Davis","year":"1980","unstructured":"S. Davis, P. Mermelstein, Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Signal Process. 28(4), 357\u2013366 (1980)","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"2770_CR10","first-page":"309","volume":"34","author":"PC Doyle","year":"1997","unstructured":"P.C. Doyle, H.A. Leeper, A.L. Kotler, N. Thomas-Stonell, C. O\u2019Neill, M.C. Dylke, K. Roll, Dysarthric speech: A comparison of computerized speech recognition and listener intelligibility. J. Rehabil. Res. Dev. 34, 309\u2013316 (1997)","journal-title":"J. Rehabil. Res. Dev."},{"key":"2770_CR11","unstructured":"Dysarthria. American Speech-Language-Hearing Association. https:\/\/www.asha.org\/public\/speech\/disorders\/dysarthria\/"},{"issue":"3","key":"2770_CR12","doi-asserted-by":"publisher","first-page":"165","DOI":"10.3109\/13682828009112541","volume":"15","author":"P Enderby","year":"1980","unstructured":"P. Enderby, Frenchay dysarthria assessment. Br. J. Disord. Commun. 15(3), 165\u2013173 (1980)","journal-title":"Br. J. Disord. Commun."},{"key":"2770_CR13","doi-asserted-by":"publisher","first-page":"103976","DOI":"10.1016\/j.engappai.2020.103976","volume":"96","author":"M Fern\u00e1ndez-D\u00edaz","year":"2020","unstructured":"M. Fern\u00e1ndez-D\u00edaz, A. Gallardo-Antol\u00edn, An attention long short-term memory based system for automatic classification of speech intelligibility. Eng. Appl. Artif. Intell. 96, 103976 (2020)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"11","key":"2770_CR14","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.3390\/brainsci12111471","volume":"12","author":"C Fougeron","year":"2022","unstructured":"C. Fougeron, I. Kodrasi, M. Laganaro, Differentiation of motor speech disorders through the seven deviance scores from MonPaGe-20. Brain Sci. 12(11), 1471 (2022)","journal-title":"Brain Sci."},{"issue":"10","key":"2770_CR15","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TBME.2006.871883","volume":"53","author":"JI Godino-Llorente","year":"2006","unstructured":"J.I. Godino-Llorente, P. Gomez-Vilda, M. Blanco-Velasco, Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Trans. Biomed. Eng. 53(10), 1943\u20131953 (2006)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2770_CR16","first-page":"321","volume-title":"Deep Learning (Adaptive Computation and Machine Learning Series)","author":"I Goodfellow","year":"2017","unstructured":"I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (Adaptive Computation and Machine Learning Series) (Cambridge Massachusetts, 2017), pp.321\u2013359"},{"key":"2770_CR17","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neunet.2021.02.008","volume":"139","author":"S Gupta","year":"2021","unstructured":"S. Gupta, A.T. Patil, M. Purohit, M. Parmar, M. Patel, H.A. Patil, R.C. Guido, Residual neural network precisely quantifies dysarthria severity-level based on short-duration speech segments. Neural Netw. 139, 105\u2013117 (2021)","journal-title":"Neural Netw."},{"issue":"19","key":"2770_CR18","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.3390\/app10196999","volume":"10","author":"A Hernandez","year":"2020","unstructured":"A. Hernandez, S. Kim, M. Chung, Prosody-based measures for automatic severity assessment of dysarthric speech. Appl. Sci. 10(19), 6999 (2020)","journal-title":"Appl. Sci."},{"issue":"2\u20134","key":"2770_CR19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1159\/000265824","volume":"38","author":"H Hirose","year":"1986","unstructured":"H. Hirose, Pathophysiology of motor speech disorders (dysarthria). Folia Phoniatr. Logop. 38(2\u20134), 61\u201388 (1986)","journal-title":"Folia Phoniatr. Logop."},{"issue":"8","key":"2770_CR20","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.3390\/brainsci12081011","volume":"12","author":"ME Hirsch","year":"2022","unstructured":"M.E. Hirsch, A. Thompson, Y. Kim, K.L. Lansford, The reliability and validity of speech-language pathologists\u2019 estimations of intelligibility in dysarthria. Brain Sci. 12(8), 1011 (2022)","journal-title":"Brain Sci."},{"key":"2770_CR21","unstructured":"X. Huang, A. Acero, H.W. Hon, R. Reddy, Spoken language processing: A guide to theory, algorithm, and system development. Prentice hall PTR (2001)"},{"key":"2770_CR22","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TNSRE.2022.3169814","volume":"30","author":"AA Joshy","year":"2022","unstructured":"A.A. Joshy, R. Rajan, Automated dysarthria severity classification: A study on acoustic features and deep learning techniques. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 1147\u20131157 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"2770_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.specom.2022.12.004","volume":"147","author":"AA Joshy","year":"2023","unstructured":"A.A. Joshy, R. Rajan, Dysarthria severity classification using multi-head attention and multi-task learning. Speech Commun. 147, 1\u201311 (2023)","journal-title":"Speech Commun."},{"key":"2770_CR24","doi-asserted-by":"crossref","unstructured":"A.A. Joshy, R. Rajan, Automated dysarthria severity classification using deep learning frameworks.In 28th European Signal Processing Conference (EUSIPCO) :116\u2013120 (2021)","DOI":"10.23919\/Eusipco47968.2020.9287741"},{"key":"2770_CR25","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-031-20980-2_28","volume-title":"Speech and Computer: 24th International Conference, SPECOM 2022, Gurugram, India, November 14\u201316, 2022, Proceedings","author":"A Kachhi","year":"2022","unstructured":"A. Kachhi, A. Therattil, A.T. Patil, H.B. Sailor, H.A. Patil, Significance of\u00a0energy features for\u00a0severity classification of\u00a0dysarthria, in Speech and Computer: 24th International Conference, SPECOM 2022, Gurugram, India, November 14\u201316, 2022, Proceedings. ed. by S.R. Mahadeva Prasanna, K. Alexey Karpov, S.S. Samudravijaya, Agrawal, (Springer, Cham, 2022), pp.325\u2013337"},{"key":"2770_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-017-8832-8_38","author":"KL Kadi","year":"2014","unstructured":"K.L. Kadi, S.A. Selouani, B. Boudraa, M. Boudraa, Automated diagnosis and assessment of dysarthric speech using relevant prosodic features. Trans. Eng. Technol. (2014). https:\/\/doi.org\/10.1007\/978-94-017-8832-8_38","journal-title":"Trans. Eng. Technol."},{"issue":"1","key":"2770_CR27","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.bbe.2015.11.004","volume":"36","author":"KL Kadi","year":"2016","unstructured":"K.L. Kadi, S.A. Selouani, B. Boudraa, M. Boudraa, Fully automated speaker identification and intelligibility assessment in dysarthria disease using auditory knowledge. Biocybern. Biomed. Eng. 36(1), 233\u2013247 (2016)","journal-title":"Biocybern. Biomed. Eng."},{"issue":"3","key":"2770_CR28","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/S0021-9924(99)00004-0","volume":"32","author":"RD Kent","year":"1999","unstructured":"R.D. Kent, G. Weismer, J.F. Kent, H.K. Vorperian, J.R. Duffy, Acoustic studies of dysarthric speech: methods, progress, and potential. J. Commun. Disord. 32(3), 141\u2013186 (1999)","journal-title":"J. Commun. Disord."},{"key":"2770_CR29","doi-asserted-by":"crossref","unstructured":"H. Kim, M. Hasegawa-Johnson, A. Perlman, J. Gunderson, T.S. Huang, K. Watkin, S. Frame, Dysarthric speech database for universal access research. In Ninth Annual Conference of the International Speech Communication Association (2008)","DOI":"10.21437\/Interspeech.2008-480"},{"key":"2770_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.specom.2020.06.003","volume":"123","author":"NP Narendra","year":"2020","unstructured":"N.P. Narendra, P. Alku, Automatic intelligibility assessment of dysarthric speech using glottal parameters. Speech Commun. 123, 1\u20139 (2020)","journal-title":"Speech Commun."},{"key":"2770_CR31","doi-asserted-by":"crossref","unstructured":"M.S. Paja, T.H. Falk, Automated dysarthria severity classification for improved objective intelligibility assessment of spastic dysarthric speech In Thirteenth Annual Conference of the International Speech Communication Association (2012)","DOI":"10.21437\/Interspeech.2012-26"},{"key":"2770_CR32","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.procs.2020.04.017","volume":"171","author":"E Patel","year":"2020","unstructured":"E. Patel, D.S. Kushwaha, Clustering cloud workloads: K-means vs gaussian mixture model. Proc. Comput. Sci. 171, 158\u2013167 (2020)","journal-title":"Proc. Comput. Sci."},{"issue":"8","key":"2770_CR33","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1016\/j.medengphy.2005.11.002","volume":"28","author":"PD Polur","year":"2006","unstructured":"P.D. Polur, G.E. Miller, Investigation of an HMM\/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals. Med. Eng. Phys. 28(8), 741\u2013748 (2006)","journal-title":"Med. Eng. Phys."},{"issue":"13","key":"2770_CR34","doi-asserted-by":"publisher","first-page":"3492","DOI":"10.1158\/0008-5472.CAN-19-0573","volume":"79","author":"I Prabakaran","year":"2019","unstructured":"I. Prabakaran, Z. Wu, C. Lee, B. Tong, S. Steeman, G. Koo, P.J. Zhang, M.A. Guvakova, Gaussian mixture models for probabilistic classification of breast cancer. Can. Res. 79(13), 3492\u20133502 (2019)","journal-title":"Can. Res."},{"key":"2770_CR35","unstructured":"L. Rabiner, Fundamentals of speech recognition PTR Prentice Hall (1993)"},{"key":"2770_CR36","unstructured":"S.J. Robertson, Dysarthria profile. Communication Skill Builders (1987)"},{"key":"2770_CR37","doi-asserted-by":"crossref","unstructured":"S A. Selouani, H. Dahmani, R. Amami, H. Hamam, Dysarthric speech classification using hierarchical multilayer perceptrons and posterior rhythmic features. InSoft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO :437\u2013444 (2011)","DOI":"10.1007\/978-3-642-19644-7_46"},{"issue":"1","key":"2770_CR38","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.aei.2014.01.001","volume":"28","author":"SR Shahamiri","year":"2014","unstructured":"S.R. Shahamiri, S.S. Salim, Artificial neural networks as speech recognisers for dysarthric speech: Identifying the best-performing set of MFCC parameters and studying a speaker-independent approach. Adv. Eng. Inform. 28(1), 102\u2013110 (2014)","journal-title":"Adv. Eng. Inform."},{"issue":"12","key":"2770_CR39","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1109\/10.250584","volume":"40","author":"BK Sy","year":"1993","unstructured":"B.K. Sy, D.M. Horowitz, A statistical causal model for the assessment of dysarthric speech and the utility of computer-based speech recognition. IEEE Trans. Biomed. Eng. 40(12), 1282\u20131298 (1993)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2770_CR40","doi-asserted-by":"crossref","unstructured":"H. Tong, Automatic assessment of dysarthric severity level using audio-video cross-modal approach in deep learning. (Master's thesis) (2020)","DOI":"10.21437\/Interspeech.2020-1997"},{"issue":"1","key":"2770_CR41","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.jvoice.2009.08.002","volume":"25","author":"X Wang","year":"2011","unstructured":"X. Wang, J. Zhang, Y. Yan, Discrimination between pathological and normal voices using GMM-SVM approach. J. Voice 25(1), 38\u201343 (2011)","journal-title":"J. Voice"},{"key":"2770_CR42","doi-asserted-by":"crossref","unstructured":"E.J. Yeo, K. Choi, S. Kim, M. Chung, Automatic severity classification of dysarthric speech by using self-supervised model with multi-task learning. In\u00a0ICASSP 2023\u20132023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\u00a0(pp. 1\u20135). IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10094605"},{"key":"2770_CR43","unstructured":"K.M. Yorkston, D.R. Beukelman, C. Traynor, Assessment of intelligibility of dysarthric speech. Austin, TX: Pro-ed (1984)"}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-024-02770-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-024-02770-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-024-02770-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T12:15:29Z","timestamp":1728303329000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-024-02770-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":43,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["2770"],"URL":"https:\/\/doi.org\/10.1007\/s00034-024-02770-7","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"assertion":[{"value":"29 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}