Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 1; p. 321
• Main Authors: Świetlicka, Izabela, Kuniszyk-Jóźkowiak, Wiesława, Świetlicki, Michał
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-01-2022; MDPI
• Subjects: Algorithms; Analysis; artificial neural networks; Classification; Eigenvalues; Experiments; Humans; Multilayer perceptrons; Network topologies; Neural networks; Neural Networks, Computer; Pattern recognition systems; Principal Component Analysis; Principal components analysis; Signal processing; Speech; Speech Perception; Speech recognition; Stuttering; Variables; Voice recognition; Wavelet transforms; Poland; artificial neural networks; stuttering; speech recognition; principal component analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22010321

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-blocks before words starting with plosives, syllable repetitions, and sound-initial prolongations-was 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).