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Speech nonfluency detection and classification based on linear prediction coefficients and neural networks

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.
Rocznik
Tom
Strony
135--143
Opis fizyczny
Bibliogr. 8 poz., rys., tab.
Twórcy
autor
  • Institute of Computer Science, Marie Curie-Skłodowska University, Pl. M. Curie-Skłodowskiej 1, 20-031 Lublin, Poland
autor
autor
Bibliografia
  • [1] BLACKMAN R.B., TUKEY J.W., Particular Pairs of Windows. The measurement of power spectra from the point of view of communications engineering, Bell System Tech., New York: Dover, 1959, pp. 95–101.
  • [2] BUHMANN M.D., Radial Basis Functions: Theory and Implementations, Cambridge University, 2003, pp. 2–5.
  • [3] CHANDZLIK S., KOPICERA K., The method of neuron weight vector initial values selection in Kohonen network, Journal of Medical Informatics & Technologies, Vol. 10, 2006, pp. 189–198.
  • [4] CODELLO I., KUNISZYK–JÓŹKOWIAK W., Digital signals analysis with the LPC method, Annales UMCS Informatica. Vol. 5, Lublin, 2006, pp. 315–323.
  • [5] PROKSA R., Visualization of stages of determining cepstral factors in speech recognition systems, Journal of Medical Informatics & Technologies, Vol. 13, 2009, pp. 121–128.
  • [6] RABINER L.R., SCHAFER R.W., Digital Processing of Speech Signals, Prentice Hall, New Jersey, 1978, pp. 396–461.
  • [7] SZCZUROWSKA I., KUNISZYK–JÓŹKOWIAK W., SMOŁKA E., Speech nonfluency detection using Kohonen networks, Neural Computing & Applications, Springer, Vol. 18, Number 7, London, 2009, pp. 677–687.
  • [8] TEBELSKIS J., Speech Recognition using Neural Networks, Ph. D. Dissertation, Carnegie Mellon University, Pittsburgh, 1995, pp. 101–146.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0017-0021
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