{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T13:04:22Z","timestamp":1773493462649,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The presented paper introduces principal component analysis application for dimensionality reduction of variables describing speech signal and applicability of obtained results for the disturbed and fluent speech recognition process. A set of fluent speech signals and three speech disturbances\u2014blocks before words starting with plosives, syllable repetitions, and sound-initial prolongations\u2014was transformed using principal component analysis. The result was a model containing four principal components describing analysed utterances. Distances between standardised original variables and elements of the observation matrix in a new system of coordinates were calculated and then applied in the recognition process. As a classifying algorithm, the multilayer perceptron network was used. Achieved results were compared with outcomes from previous experiments where speech samples were parameterised with the Kohonen network application. The classifying network achieved overall accuracy at 76% (from 50% to 91%, depending on the dysfluency type).<\/jats:p>","DOI":"10.3390\/s22010321","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1053-0948","authenticated-orcid":false,"given":"Izabela","family":"\u015awietlicka","sequence":"first","affiliation":[{"name":"Department of Biophysics, University of Life Sciences, Akademicka 13, 20-950 Lublin, Poland"}]},{"given":"Wies\u0142awa","family":"Kuniszyk-J\u00f3\u017akowiak","sequence":"additional","affiliation":[{"name":"Faculty of Physical Education and Health in Bia\u0142a Podlaska, J\u00f3zef Pi\u0142sudski University of Physical Education in Warsaw, Akademicka 2, 21-500 Bia\u0142a Podlaska, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3625-542X","authenticated-orcid":false,"given":"Micha\u0142","family":"\u015awietlicki","sequence":"additional","affiliation":[{"name":"Department of Applied Physics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"ref_1","unstructured":"Howell, P., and Sackin, S. 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