{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:49:52Z","timestamp":1777704592648,"version":"3.51.4"},"reference-count":18,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2020,12,4]]},"abstract":"<jats:p>Dysarthria is a speech disorder caused by stroke, Parkinson\u2019s disease, neurological injury, or tumors that damage the nervous system and weaken the speech quality. Developing a unique voice command system for Dysarthric speech helps to recognize impaired speech and convert them into text or input commands. Hidden Markov Model (HMM) is one of the widely used generative model-based classifiers for Dysarthric speech recognition. But due to insufficient training data, HMM doesn\u2019t provide optimal results on overlapping classes. We propose an ensemble Gaussian mixture model to recognize impaired speech more accurately. Our model converts the sequence of feature vectors into a fixed dimensional representation of patterns with varying lengths. The performance efficiency of the proposed model is evaluated on the Dysarthric UA-speech benchmark dataset. The discriminatory information provided by the proposed approach yields better classification accuracy even for shallow intelligibility words compared to conventional HMM.<\/jats:p>","DOI":"10.3233\/jifs-189139","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T10:02:37Z","timestamp":1599559357000},"page":"8181-8189","source":"Crossref","is-referenced-by-count":1,"title":["Ensemble Gaussian mixture model-based special voice command cognitive computing intelligent system"],"prefix":"10.1177","volume":"39","author":[{"given":"P.","family":"Saravanan","sequence":"first","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, India"}]},{"given":"E.","family":"Sri Ram","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, India"}]},{"given":"Saikishor","family":"Jangiti","sequence":"additional","affiliation":[{"name":"Birla Institute of Technology and Science, Pilani, India"}]},{"given":"E.","family":"Ponmani","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, India"}]},{"given":"Logesh","family":"Ravi","sequence":"additional","affiliation":[{"name":"Birla Institute of Technology and Science, Pilani, India"}]},{"given":"V.","family":"Subramaniyaswamy","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/JIFS-189139_ref2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1080\/14417040600970606","article-title":"Methods of speech therapy treatment for stable Dysarthria: A review","volume":"9","author":"Palmer","year":"2007","journal-title":"Advances in Speech-Language Pathology"},{"key":"10.3233\/JIFS-189139_ref3","unstructured":"Minifie F.D. , ed. Introduction to communication sciences and disorders. Singular Publishing Group, Incorporated, 1994."},{"key":"10.3233\/JIFS-189139_ref4","doi-asserted-by":"crossref","unstructured":"Ren J. and Liu M. , An Automatic Dysarthric Speech Recognition Approach using Deep Neural Networks, International Journal of Advanced Computer Science and Applications 8(12) (2017).","DOI":"10.14569\/IJACSA.2017.081207"},{"key":"10.3233\/JIFS-189139_ref5","doi-asserted-by":"crossref","unstructured":"Akta\u015f F. and Buzluca F. , A Learning-Based Bug Prediction Method for Object-Oriented Systems, In 2018 IEEE\/ACIS 17th International Conference on Computer and Information Science (ICIS) 2018 Jun 6 (pp. 217\u2013223). IEEE.","DOI":"10.1109\/ICIS.2018.8466535"},{"issue":"1","key":"10.3233\/JIFS-189139_ref7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s00180-012-0374-5","article-title":"Ensemble Gaussian mixture models for probability density estimation","volume":"28","author":"Glodek","year":"2013","journal-title":"Computational Statistics"},{"key":"10.3233\/JIFS-189139_ref8","doi-asserted-by":"crossref","unstructured":"Janouek J. , Gajdo P. , Radeck\u00fd M. and Sn\u00e1el V. , Gaussian mixture model cluster forest, In 2015, IEEE 14th International Conference on Machine Learning and Applications (ICMLA) 2015 Dec 9 (pp. 1019\u20131023). IEEE.","DOI":"10.1109\/ICMLA.2015.12"},{"issue":"8","key":"10.3233\/JIFS-189139_ref12","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.medengphy.2005.11.002","article-title":"Investigation of an HMM\/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals","volume":"28","author":"Polur","year":"2006","journal-title":"Medical engineering & physics"},{"issue":"5","key":"10.3233\/JIFS-189139_ref13","doi-asserted-by":"crossref","first-page":"643","DOI":"10.4218\/etrij.2017-0260","article-title":"Dysarthric speaker identification with different degrees of dysarthria severity using deep belief networks","volume":"40","author":"Farhadipour","year":"2018","journal-title":"ETRI Journal"},{"key":"10.3233\/JIFS-189139_ref15","doi-asserted-by":"crossref","unstructured":"Hasegawa-Johnson M. , Gunderson J. , Perlman A. and Huang T. , HMM-based and SVM-based recognition of the speech of talkers with spastic Dysarthria. In 2006, IEEE International Conference on Acoustics Speech and Signal Processing Proceedings 2006 May 14 (Vol. 3, pp. III\u2013III). IEEE.","DOI":"10.1109\/ICASSP.2006.1660840"},{"key":"10.3233\/JIFS-189139_ref17","doi-asserted-by":"crossref","unstructured":"Nakashika T. , Yoshioka T. , Takiguchi T. , Ariki Y. , Duffner S. and Garcia C. , Dysarthric speech recognition using a convolutive bottleneck network, In 2014 12th International Conference on Signal Processing (ICSP) 2014 Oct 19 (pp. 505\u2013509). IEEE.","DOI":"10.1109\/ICOSP.2014.7015056"},{"issue":"9","key":"10.3233\/JIFS-189139_ref18","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1109\/TNSRE.2017.2681691","article-title":"Regularized speaker adaptation of KL-HMM for dysarthric speech recognition","volume":"25","author":"Kim","year":"2017","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"10.3233\/JIFS-189139_ref19","unstructured":"Miller G.E. , Etter B.D. and Bartholomew J.C. , Analysis of voice processing for the control of devices to aid the disabled, In Proceedings of the 12th Annual RESNA Conference: 410-412RESNA Press1989."},{"issue":"5","key":"10.3233\/JIFS-189139_ref23","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/TNSRE.2014.2309336","article-title":"A multi-views multi-learners approach towards dysarthric speech recognition using multi-nets artificial neural networks","volume":"22","author":"Shahamiri","year":"2014","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"613","key":"10.3233\/JIFS-189139_ref24","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.1256\/qj.05.115","article-title":"An overview of the variational assimilation in the ALA-DIN\/France numerical weather-prediction system","volume":"131","author":"Fischer","year":"2005","journal-title":"Quarterly Journal of the Royal Meteorological Society: A Journal of the atmospheric sciences, applied meteorology and physical oceanography"},{"issue":"1","key":"10.3233\/JIFS-189139_ref25","first-page":"25","article-title":"Access interface strategies","volume":"24","author":"Fager","year":"2012","journal-title":"Technology"},{"issue":"1","key":"10.3233\/JIFS-189139_ref27","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3233\/PRM-2012-0196","article-title":"Access to augmentative and alternative communication: New technologies and clinical decision-making","volume":"5","author":"Fager","year":"2012","journal-title":"Journal of pediatric rehabilitation medicine"},{"key":"10.3233\/JIFS-189139_ref28","doi-asserted-by":"crossref","unstructured":"Mengistu K.T. and Rudzicz F. , Adapting acoustic and lexical models to dysarthric speech, In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2011 May 22 (pp. 4924\u20134927). IEEE.","DOI":"10.1109\/ICASSP.2011.5947460"},{"issue":"6","key":"10.3233\/JIFS-189139_ref29","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.csl.2012.10.002","article-title":"Acoustic model adaptation using in-domain background models for dysarthric speech recognition","volume":"27","author":"Sharma","year":"2013","journal-title":"Computer Speech & Language"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-189139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:33Z","timestamp":1777455693000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-189139"}},"subtitle":[],"editor":[{"given":"Vijayakumar","family":"Varadarajan","sequence":"additional","affiliation":[]},{"given":"Piet","family":"Kommers","sequence":"additional","affiliation":[]},{"given":"Vincenzo","family":"Piuri","sequence":"additional","affiliation":[]},{"given":"V.","family":"Subramaniyaswamy","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,12,4]]},"references-count":18,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-189139","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,4]]}}}