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The scattering features are encoded using Fisher vectors to obtain a single fixed-size feature vector per utterance. Support vector machine and feed-forward neural network classifiers are trained using the utterance-level features to perform the detection task (healthy vs PD). The results obtained with the PC-GITA database revealed that the proposed approach shows better results in comparison to the state-of-the-art techniques. The best classification accuracy of 87% was achieved with the proposed approach using speech from a text reading task.<\/jats:p>","DOI":"10.1121\/10.0036660","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T12:54:38Z","timestamp":1747659278000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":2,"title":["Automatic detection of Parkinsonian speech using wavelet scattering features"],"prefix":"10.1121","volume":"5","author":[{"given":"Mittapalle","family":"Kiran Reddy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Information Technology Raichur 1 , 584135 Karnataka,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paavo","family":"Alku","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Aalto University 2 , FI-00076 Espoo, kiranmittapalle@iiitr.ac.in , paavo.alku@aalto.fi","place":["Finland"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"231","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"issue":"16","key":"2025051908543265800_c1","doi-asserted-by":"publisher","first-page":"4114","DOI":"10.1109\/TSP.2014.2326991","article-title":"Deep scattering spectrum","volume":"62","year":"2014","journal-title":"IEEE Trans. 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