{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T13:27:19Z","timestamp":1769347639074,"version":"3.49.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"DST FIST FUND","award":["SR\/FST\/ET-I\/2018\/221(C)"],"award-info":[{"award-number":["SR\/FST\/ET-I\/2018\/221(C)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Pers Commun"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11277-024-11029-y","type":"journal-article","created":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T02:56:11Z","timestamp":1713668171000},"page":"2315-2346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Strategic Approach for Robust Dysarthric Speech Recognition"],"prefix":"10.1007","volume":"134","author":[{"given":"A.","family":"Revathi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N.","family":"Sasikaladevi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Arunprasanth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1574-3045","authenticated-orcid":false,"given":"Rengarajan","family":"Amirtharajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,20]]},"reference":[{"issue":"3","key":"11029_CR1","first-page":"2287","volume":"8","author":"L Cespedes-Simangas","year":"2021","unstructured":"Cespedes-Simangas, L., Uribe-Obregon, C., & Cabanillas-Carbonell, M. (2021). Analysis of speech therapy systems for children with physical disabilities and speech disorders: A systematic review. European Journal of Molecular & Clinical Medicine, 8(3), 2287\u20132301.","journal-title":"European Journal of Molecular & Clinical Medicine"},{"key":"11029_CR2","doi-asserted-by":"crossref","unstructured":"Takashima, Y., Takiguchi, T., & Ariki, Y. (2019). End-to-end dysarthric speech recognition using multiple databases. In ICASSP 2019\u20132019 IIEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 6395\u20136399","DOI":"10.1109\/ICASSP.2019.8683803"},{"issue":"4","key":"11029_CR3","doi-asserted-by":"publisher","first-page":"352","DOI":"10.4103\/aian.AIAN_130_17","volume":"20","author":"MG Thoppil","year":"2017","unstructured":"Thoppil, M. G., Kumar, C. S., Kumar, A., & Amose, J. (2017). Speech signal analysis and pattern recognition in diagnosis of dysarthria. Annals of Indian Academy of Neurology, 20(4), 352.","journal-title":"Annals of Indian Academy of Neurology"},{"key":"11029_CR4","doi-asserted-by":"crossref","unstructured":"Aihara, R., Takiguchi, T., & Ariki, Y. (2017). Phoneme-discriminative features for dysarthric speech conversion. In Interspeech, pp 3374\u20133378","DOI":"10.21437\/Interspeech.2017-664"},{"key":"11029_CR5","doi-asserted-by":"crossref","unstructured":"Jiao, Y., Tu, M., Berisha, V., & Liss, J. (2018). Simulating dysarthric speech for training data augmentation in clinical speech applications. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 6009\u20136013","DOI":"10.1109\/ICASSP.2018.8462290"},{"key":"11029_CR6","doi-asserted-by":"crossref","unstructured":"Takashima, Y., Nakashika, T., Takiguchi, T., & Ariki, Y. (2015). Feature extraction using pre-trained convolutive bottleneck nets for dysarthric speech recognition. In 2015 23rd European Signal Processing Conference (EUSIPCO), IEEE, pp 1411\u20131415","DOI":"10.1109\/EUSIPCO.2015.7362616"},{"key":"11029_CR7","doi-asserted-by":"crossref","unstructured":"Espana-Bonet, C., & Fonollosa, J. A. (2016). Automatic speech recognition with deep neural networks for impaired speech. In Advances in Speech and Language Technologies for Iberian Languages: Third International Conference, IberSPEECH 2016, Lisbon, Portugal, November 23\u201325, 2016, Proceedings 3, Springer International Publishing, pp 97\u2013107","DOI":"10.1007\/978-3-319-49169-1_10"},{"key":"11029_CR8","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s10772-011-9104-6","volume":"15","author":"SA Selouani","year":"2012","unstructured":"Selouani, S. A., Dahmani, H., Amami, R., & Hamam, H. (2012). Using speech rhythm knowledge to improve dysarthric speech recognition. International Journal of Speech Technology, 15, 57\u201364.","journal-title":"International Journal of Speech Technology"},{"issue":"6","key":"11029_CR9","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1016\/j.csl.2012.11.001","volume":"27","author":"F Rudzicz","year":"2013","unstructured":"Rudzicz, F. (2013). Adjusting dysarthric speech signals to be more intelligible. Computer Speech & Language, 27(6), 1163\u20131177.","journal-title":"Computer Speech & Language"},{"issue":"1","key":"11029_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1687-4722-2014-5","volume":"2014","author":"R Aihara","year":"2014","unstructured":"Aihara, R., Takashima, R., Takiguchi, T., & Ariki, Y. (2014). A preliminary demonstration of exemplar-based voice conversion for articulation disorders using an individuality-preserving dictionary. EURASIP Journal on Audio, Speech, and Music Processing, 2014(1), 1\u201310.","journal-title":"EURASIP Journal on Audio, Speech, and Music Processing"},{"issue":"4","key":"11029_CR11","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/TASL.2010.2072499","volume":"19","author":"F Rudzicz","year":"2010","unstructured":"Rudzicz, F. (2010). Articulatory knowledge in the recognition of dysarthric speech. IEEE Transactions on Audio, Speech, and Language Processing, 19(4), 947\u2013960.","journal-title":"IEEE Transactions on Audio, Speech, and Language Processing"},{"key":"11029_CR12","doi-asserted-by":"crossref","unstructured":"Tu, M., Berisha, V., & Liss, J. (2017). Interpretable objective assessment of dysarthric speech based on deep neural networks. In Interspeech, pp 1849\u20131853","DOI":"10.21437\/Interspeech.2017-1222"},{"key":"11029_CR13","unstructured":"Rudzicz, F. (2011). Acoustic transformations to improve the intelligibility of dysarthric speech. In Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies, pp 11\u201321"},{"issue":"13","key":"11029_CR14","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3346\/jkms.2019.34.e108","volume":"34","author":"SH Lee","year":"2019","unstructured":"Lee, S. H., Kim, M., Seo, H. G., Oh, B. M., Lee, G., & Leigh, J. H. (2019). Assessment of dysarthria using one-word speech recognition with hidden markov models. Journal of Korean Medical Science, 34(13), 108.","journal-title":"Journal of Korean Medical Science"},{"issue":"3","key":"11029_CR15","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1109\/TASLP.2016.2641904","volume":"25","author":"CS Doire","year":"2016","unstructured":"Doire, C. S., Brookes, M., Naylor, P. A., Hicks, C. M., Betts, D., Dmour, M. A., & Jensen, S. H. (2016). Single-channel online enhancement of speech corrupted by reverberation and noise. IEEE\/ACM Transactions on Audio, Speech, and Language Processing, 25(3), 572\u2013587.","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"issue":"2","key":"11029_CR16","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1109\/TASSP.1985.1164550","volume":"33","author":"Y Ephraim","year":"1985","unstructured":"Ephraim, Y., & Malah, D. (1985). Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(2), 443\u2013445.","journal-title":"IEEE Transactions on Acoustics, Speech, and Signal Processing"},{"issue":"6","key":"11029_CR17","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1109\/TASSP.1984.1164453","volume":"32","author":"Y Ephraim","year":"1984","unstructured":"Ephraim, Y., & Malah, D. (1984). Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32(6), 1109\u20131121.","journal-title":"IEEE Transactions on Acoustics, Speech, and Signal Processing"},{"key":"11029_CR18","doi-asserted-by":"crossref","unstructured":"Lallouani, A., Gabrea, M., & Gargour, C. S. (2004). Wavelet based speech enhancement using two different threshold-based denoising algorithms. In Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No. 04CH37513), IEEE, vol. 1, pp 315\u2013318","DOI":"10.1109\/CCECE.2004.1345019"},{"key":"11029_CR19","unstructured":"Islam, M. T., Shahnaz, C., Zhu, W. P., & Ahmad, M. O. (2018). Enhancement of noisy speech with low speech distortion based on probabilistic geometric spectral subtraction. arXiv preprint arXiv:1802.05125."},{"issue":"6","key":"11029_CR20","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.specom.2008.01.003","volume":"50","author":"Y Lu","year":"2008","unstructured":"Lu, Y., & Loizou, P. C. (2008). A geometric approach to spectral subtraction. Speech communication, 50(6), 453\u2013466.","journal-title":"Speech communication"},{"key":"11029_CR21","doi-asserted-by":"crossref","unstructured":"Stark, A. P., W\u00f3jcicki, K. K., Lyons, J. G., & Paliwal, K. K. (2008). Noise driven short-time phase spectrum compensation procedure for speech enhancement. In Ninth Annual Conference of the International Speech Communication Association","DOI":"10.21437\/Interspeech.2008-163"},{"key":"11029_CR22","doi-asserted-by":"crossref","unstructured":"Kim, H., Hasegawa-Johnson, M., Perlman, A., Gunderson, J., Huang, T. S., Watkin, K., & Frame, S. (2008). Dysarthric speech database for universal access research. In Ninth Annual Conference of the International Speech Communication Association","DOI":"10.21437\/Interspeech.2008-480"},{"key":"11029_CR23","doi-asserted-by":"publisher","first-page":"20787","DOI":"10.1007\/s11042-019-7329-6","volume":"78","author":"R Arunachalam","year":"2019","unstructured":"Arunachalam, R. (2019). A strategic approach to recognize the speech of the children with hearing impairment: Different sets of features and models. Multimedia Tools and Applications, 78, 20787\u201320808.","journal-title":"Multimedia Tools and Applications"},{"key":"11029_CR24","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.specom.2018.04.005","volume":"99","author":"V Despotovic","year":"2018","unstructured":"Despotovic, V., Walter, O., & Haeb-Umbach, R. (2018). Machine learning techniques for semantic analysis of dysarthric speech: An experimental study. Speech Communication, 99, 242\u2013251.","journal-title":"Speech Communication"},{"key":"11029_CR25","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.specom.2019.04.003","volume":"110","author":"NP Narendra","year":"2019","unstructured":"Narendra, N. P., & Alku, P. (2019). Dysarthric speech classification from coded telephone speech using glottal features. Speech Communication, 110, 47\u201355.","journal-title":"Speech Communication"},{"key":"11029_CR26","doi-asserted-by":"publisher","first-page":"101117","DOI":"10.1016\/j.csl.2020.101117","volume":"65","author":"NP Narendra","year":"2021","unstructured":"Narendra, N. P., & Alku, P. (2021). Automatic assessment of intelligibility in speakers with dysarthria from coded telephone speech using glottal features. Computer Speech & Language, 65, 101117.","journal-title":"Computer Speech & Language"},{"key":"11029_CR27","doi-asserted-by":"publisher","first-page":"5543","DOI":"10.1007\/s00034-020-01419-5","volume":"39","author":"G Diwakar","year":"2020","unstructured":"Diwakar, G., & Karjigi, V. (2020). Improving speech to text alignment based on repetition detection for dysarthric speech. Circuits, Systems, and Signal Processing, 39, 5543\u20135567.","journal-title":"Circuits, Systems, and Signal Processing"},{"key":"11029_CR28","doi-asserted-by":"publisher","first-page":"616062","DOI":"10.3389\/fneur.2020.616062","volume":"11","author":"F Cavallieri","year":"2021","unstructured":"Cavallieri, F., Budriesi, C., Gessani, A., Contardi, S., Fioravanti, V., Menozzi, E., & Antonelli, F. (2021). Dopaminergic treatment effects on dysarthric speech: Acoustic analysis in a cohort of patients with advanced Parkinson\u2019s disease. Frontiers in Neurology, 11, 616062.","journal-title":"Frontiers in Neurology"},{"issue":"2","key":"11029_CR29","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1044\/2020_JSLHR-20-00313","volume":"64","author":"ME Hirsch","year":"2021","unstructured":"Hirsch, M. E., Lansford, K. L., Barrett, T. S., & Borrie, S. A. (2021). Generalized learning of dysarthric speech between male and female talkers. Journal of Speech, Language, and Hearing Research, 64(2), 444\u2013451.","journal-title":"Journal of Speech, Language, and Hearing Research"},{"key":"11029_CR30","first-page":"1","volume":"14","author":"A Hu","year":"2021","unstructured":"Hu, A., Phadnis, D., & Shahamiri, S. R. (2021). Generating synthetic dysarthric speech to overcome dysarthria acoustic data scarcity. Journal of Ambient Intelligence and Humanized Computing, 14, 1\u201318.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"11029_CR31","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1109\/LSP.2021.3108509","volume":"28","author":"I Kodrasi","year":"2021","unstructured":"Kodrasi, I. (2021). Temporal envelope and fine structure cues for dysarthric speech detection using CNNs. IEEE Signal Processing Letters, 28, 1853\u20131857.","journal-title":"IEEE Signal Processing Letters"},{"key":"11029_CR32","first-page":"2267","volume":"29","author":"S Liu","year":"2021","unstructured":"Liu, S., Geng, M., Hu, S., Xie, X., Cui, M., Yu, J., & Meng, H. (2021). Recent progress in the CUHK dysarthric speech recognition system. IEEE ACM Transactions on Audio, Speech, and Language Processing, 29, 2267\u20132281.","journal-title":"IEEE ACM Transactions on Audio, Speech, and Language Processing"},{"key":"11029_CR33","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.1109\/TASLP.2021.3090973","volume":"29","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Penttil\u00e4, N., Ihalainen, T., Lintula, J., Convey, R., & R\u00e4s\u00e4nen, O. (2021). Language-independent approach for automatic computation of vowel articulation features in dysarthric speech assessment. IEEE\/ACM Transactions on Audio, Speech, and Language Processing, 29, 2228\u20132243.","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"issue":"3","key":"11029_CR34","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1108\/SR-01-2021-0004","volume":"41","author":"M Dhanalakshmi","year":"2021","unstructured":"Dhanalakshmi, M., Nagarajan, T., & Vijayalakshmi, P. (2021). Significant sensors and parameters in assessment of dysarthric speech. Sensor Review, 41(3), 271\u2013286.","journal-title":"Sensor Review"},{"issue":"1","key":"11029_CR35","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s11277-021-08899-x","volume":"122","author":"R Rajeswari","year":"2022","unstructured":"Rajeswari, R., Devi, T., & Shalini, S. (2022). Dysarthric speech recognition using variational mode decomposition and convolutional neural networks. Wireless Personal Communications, 122(1), 293\u2013307.","journal-title":"Wireless Personal Communications"},{"key":"11029_CR36","doi-asserted-by":"publisher","first-page":"101213","DOI":"10.1016\/j.csl.2021.101213","volume":"69","author":"A Tripathi","year":"2021","unstructured":"Tripathi, A., Bhosale, S., & Kopparapu, S. K. (2021). Automatic speaker independent dysarthric speech intelligibility assessment system. Computer Speech & Language, 69, 101213.","journal-title":"Computer Speech & Language"},{"key":"11029_CR37","doi-asserted-by":"publisher","first-page":"9089","DOI":"10.1007\/s00521-020-05672-2","volume":"33","author":"BF Zaidi","year":"2021","unstructured":"Zaidi, B. F., Selouani, S. A., Boudraa, M., & Sidi Yakoub, M. (2021). Deep neural network architectures for dysarthric speech analysis and recognition. Neural Computing and Applications, 33, 9089\u20139108.","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"11029_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13636-019-0169-5","volume":"2020","author":"M Sidi Yakoub","year":"2020","unstructured":"Sidi Yakoub, M., Selouani, S. A., Zaidi, B. F., & Bouchair, A. (2020). Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network. EURASIP Journal on Audio, Speech, and Music Processing, 2020(1), 1\u20137.","journal-title":"EURASIP Journal on Audio, Speech, and Music Processing"},{"key":"11029_CR39","doi-asserted-by":"publisher","first-page":"770210","DOI":"10.3389\/fcomp.2022.770210","volume":"4","author":"HP Rowe","year":"2022","unstructured":"Rowe, H. P., Gutz, S. E., Maffei, M. F., Tomanek, K., & Green, J. R. (2022). Characterizing dysarthria diversity for automatic speech recognition: A tutorial from the clinical perspective. Frontiers in Computer Science, 4, 770210.","journal-title":"Frontiers in Computer Science"},{"key":"11029_CR40","doi-asserted-by":"crossref","unstructured":"Soleymanpour, M., Johnson, M. T., Soleymanpour, R., & Berry, J. (2022). Synthesizing dysarthric speech using multi-talker TTS for dysarthric speech recognition. arXiv preprint arXiv:2201.11571.","DOI":"10.1109\/ICASSP43922.2022.9746585"},{"key":"11029_CR41","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2017.081207","author":"J Ren","year":"2017","unstructured":"Ren, J., & Liu, M. (2017). An automatic dysarthric speech recognition approach using deep neural networks. International Journal of Advanced Computer Science and Applications. https:\/\/doi.org\/10.14569\/IJACSA.2017.081207","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"11029_CR42","doi-asserted-by":"crossref","unstructured":"Harvill, J., Issa, D., Hasegawa-Johnson, M., & Yoo, C. (2021). Synthesis of new words for improved dysarthric speech recognition on an expanded vocabulary. In ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 6428\u20136432","DOI":"10.1109\/ICASSP39728.2021.9414869"},{"issue":"1","key":"11029_CR43","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.icte.2021.07.004","volume":"8","author":"SM Sekhar","year":"2022","unstructured":"Sekhar, S. M., Kashyap, G., Bhansali, A., & Singh, K. (2022). Dysarthric-speech detection using transfer learning with convolutional neural networks. ICT Express, 8(1), 61\u201364.","journal-title":"ICT Express"},{"key":"11029_CR44","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1109\/TNSRE.2021.3076778","volume":"29","author":"SR Shahamiri","year":"2021","unstructured":"Shahamiri, S. R. (2021). Speech vision: An end-to-end deep learning-based dysarthric automatic speech recognition system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 852\u2013861.","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"13","key":"11029_CR45","doi-asserted-by":"publisher","first-page":"6212","DOI":"10.3390\/s23136212","volume":"23","author":"R Ullah","year":"2023","unstructured":"Ullah, R., Asif, M., Shah, W. A., Anjam, F., Ullah, I., Khurshaid, T., & Alibakhshikenari, M. (2023). Speech emotion recognition using convolution neural networks and multi-head convolutional transformer. Sensors, 23(13), 6212.","journal-title":"Sensors"},{"issue":"10","key":"11029_CR46","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.3390\/healthcare10101956","volume":"10","author":"DH Shih","year":"2022","unstructured":"Shih, D. H., Liao, C. H., Wu, T. W., Xu, X. Y., & Shih, M. H. (2022). Dysarthria speech detection using convolutional neural networks with gated recurrent unit. In Healthcare, 10(10), 1956.","journal-title":"In Healthcare"},{"issue":"1","key":"11029_CR47","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/s12559-022-10041-3","volume":"15","author":"K Hall","year":"2023","unstructured":"Hall, K., Huang, A., & Shahamiri, S. R. (2023). An investigation to identify optimal setup for automated assessment of dysarthric intelligibility using deep learning technologies. Cognitive Computation, 15(1), 146\u2013158.","journal-title":"Cognitive Computation"},{"issue":"3","key":"11029_CR48","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/s42979-022-01623-x","volume":"4","author":"M Latha","year":"2023","unstructured":"Latha, M., Shivakumar, M., Manjula, G., Hemakumar, M., & Kumar, M. K. (2023). Deep learning-based acoustic feature representations for dysarthric speech recognition. SN Computer Science, 4(3), 272.","journal-title":"SN Computer Science"},{"key":"11029_CR49","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.1109\/TNSRE.2023.3262001","volume":"31","author":"C Yu","year":"2023","unstructured":"Yu, C., Su, X., & Qian, Z. (2023). Multi-stage audio-visual fusion for dysarthric speech recognition with pre-trained models. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1912\u20131921.","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"4","key":"11029_CR50","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1007\/s42600-022-00239-7","volume":"38","author":"A Revathi","year":"2022","unstructured":"Revathi, A., Sasikaladevi, N., & Arunprasanth, D. (2022). Development of CNN-based robust dysarthric isolated digit recognition system by enhancing speech intelligibility. Research on Biomedical Engineering, 38(4), 1067\u20131079.","journal-title":"Research on Biomedical Engineering"},{"key":"11029_CR51","doi-asserted-by":"publisher","first-page":"119797","DOI":"10.1016\/j.eswa.2023.119797","volume":"222","author":"A Almadhor","year":"2023","unstructured":"Almadhor, A., Irfan, R., Gao, J., Saleem, N., Rauf, H. T., & Kadry, S. (2023). E2E-DASR: End-to-end deep learning-based dysarthric automatic speech recognition. Expert Systems with Applications, 222, 119797.","journal-title":"Expert Systems with Applications"},{"key":"11029_CR52","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10772-023-10019-y","volume":"26","author":"B Jolad","year":"2023","unstructured":"Jolad, B., & Khanai, R. (2023). An approach for speech enhancement with dysarthric speech recognition using optimization based machine learning frameworks. International Journal of Speech Technology, 26, 287\u2013305.","journal-title":"International Journal of Speech Technology"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11029-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-024-11029-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11029-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T14:24:23Z","timestamp":1714055063000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-024-11029-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["11029"],"URL":"https:\/\/doi.org\/10.1007\/s11277-024-11029-y","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"2 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant conflicts of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article contains no studies with human participants or animals performed by authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}