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The human vocal tract that produces a short phonetics is assumed as an all-pole Infinite impulse response system and the Line spectral frequency trajectory spectrum images represents the poles of the system and reflects the voice defects due to Parkinson\u2019s disease. It is shown that the proposed method outperforms the existing state of the art work for two different utterance tasks one for sustained phonation and another for natural running speech dataset. It is demonstrated that the Deep Convolution Neural Network results in a training accuracy of 92.5% for sustained phonation dataset and training accuracy of 99.18% for King\u2019s college running speech dataset. The validation accuracies for both the datasets are 100%. The proposed work is much better than another recent benchmark work in which Mel Frequency Cepstral Coefficient parameters are used in machine learning for Parkinson\u2019s disease detection in running speech. The high performance of the proposed method for King\u2019s college running speech dataset which is collected through mobile device voice recordings, gains attention. Rigorous performance analysis is performed for running speech dataset by using separate isolated test set for repeated 50 trials and the performance metrics are F1 score of 99.37%, sensitivity of 100%, precision of 98.75% and specificity of 99.27%.<\/jats:p>","DOI":"10.3233\/jifs-230183","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T11:18:54Z","timestamp":1688123934000},"page":"4599-4615","source":"Crossref","is-referenced-by-count":2,"title":["Deep convolution neural network based Parkinson\u2019s disease detection using line spectral frequency spectrum of running speech"],"prefix":"10.1177","volume":"45","author":[{"given":"Rani","family":"Kumari","sequence":"first","affiliation":[{"name":"School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil N\u0101du, India"}]},{"given":"Prakash","family":"Ramachandran","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil N\u0101du, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-230183_ref1","doi-asserted-by":"crossref","unstructured":"Benba A. , Jilbab A. , Hammouch A. and Sandabad S. , Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson\u2019s disease, in: 2015 International Conference on Electrical and Information Technologies (ICEIT), 2015, IEEE.","DOI":"10.1109\/EITech.2015.7163000"},{"key":"10.3233\/JIFS-230183_ref2","first-page":"3311","article-title":"Diagnosis of the parkinson disease by using deep neural network classifier","volume":"17","author":"Caliskan","year":"2017","journal-title":"IU-Journal of Electrical & Electronics Engineering"},{"key":"10.3233\/JIFS-230183_ref3","doi-asserted-by":"crossref","unstructured":"Frid A. , Hazan H. , Hilu D. , Manevitz L. , Ramig L.O. and Sapir S. , Computational diagnosis of Parkinson\u2019s disease directly from natural speech using machine learning techniques, in: 2014 IEEE International Conference on Software Science, Technology and Engineering, 2014, IEEE.","DOI":"10.1109\/SWSTE.2014.17"},{"key":"10.3233\/JIFS-230183_ref4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/8822069","article-title":"Parkinson\u2019s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier","author":"Rahman","year":"2021","journal-title":"Mobile Information Systems"},{"key":"10.3233\/JIFS-230183_ref5","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/TBME.2012.2183367","article-title":"Novel speech signal processing algorithms for high-accuracy classification of Parkinson\u2019s disease","volume":"59","author":"Tsanas","year":"2012","journal-title":"IEEE Trans. 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