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Disordered sound repetition recognition in continuous speech using CWT and Kohonen network

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Automatic disorders recognition in speech can be very helpful for therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales, the result is divided into vectors and passed into Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. The recognition ratio was increased by about 20% by adding a modification into the Kohonen network training process as well as into CWT computation algorithm. All the analysis was performed and the results were obtained using the authors' program ”WaveBlaster“, The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
Rocznik
Tom
Strony
123--130
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
Bibliografia
  • [1] AKANSU A.N, HADDAD R.A., Multiresolution signal decomposition, Academic Press, 2001.
  • [2] CODELLO I., KUNISZYK-JÓŹKOWIAK W., Wavelet analysis of speech signal, Annales UMCS Informatica, 2007, AI 6, pp. 103-115.
  • [3] CODELLO I., KUNISZYK-JÓŹKOWIAK W., KOBUS A., Kohonen network application in speech analysis algorithm, Annales UMCS Informatica, 2010 (Accepted paper).
  • [4] CODELLO I., KUNISZYK-JÓŹKOWIAK W., SMOŁKA E., KOBUS A., Prolongation Recognition in Disordered Speech, Proceedings of International Conference on Fuzzy Computation, Valencia, Spain, October 2010, pp. 392-398.
  • [5] GARFIELD, S., ELSHAW M., AND WERMTER S., Self-orgazizing networks for classification learning from normal and aphasic speech, 23rd Conference of the Cognitive Science Society, Edinburgh, Scotland, 2001.
  • [6] GOLD, B., MORGAN, N., Speech and audio signal processing, John Wiley & Sons, INC, 2000.
  • [7] GOUPILLAUD P., GROSSMANN A., MORLET J., Cycle-octave and related transforms in seismic signal analysis, Geoexploration, Vol. 23, 1984-1985, pp. 85-102.
  • [8] HORZYK A, TADEUSIEWICZ R, Self-optimizing neural networks, Advances in neural networks - ISNN 2004, pt. 1, Lecture notes in computer science 3173, pp. 150-155.
  • [9] HORZYK A, TADEUSIEWICZ R, Mechanisms, symbols and models underlying cognition, Proceedings, Lecture notes in Computer Science, pt. 1, 3561, 2005, pp. 156-165.
  • [10] KOHONEN, T., Self-Organizing Maps, 34, 2001, pp.2173-2179.
  • [11] NAYAK J., BHAT P.S., ACHARYA R., AITHAL U.V., Classification and analysis of speech abnormalities, Elsevier SAS, Vol. 26, Issues 5-6, 2005, pp. 319-327.
  • [12] SMITH J., ABEL J., Bark and ERB Bilinear Transforms, IEEE Transactions on Speech and Audio Processing, November, 1999.
  • [13] SZCZUROWSKA I., KUNISZYK JÓŹKOWIAK W., SMOŁKA E., Speech nonfluency detection using Kohonen networks, Neural Computing and Application, Vol. 18, No. 7, 2009, pp. 677-687.
  • [14] SZCZUROWSKA I., KUNISZYK-JÓŹKOWIAK W., SMOŁKA E., Application of Artificial Neural Networks In Speech Nonfluency Recognition, Polish Jurnal of Environmental Studies, Vol. 16, No. 4A, 2007 pp. 335-338.
  • [15] SUSZYŃSKI W., KUNISZYK JÓŹKOWIAK W., SMOŁKA E., DZIEŃKOWSKI M., Automatic recognition of non-fluent stops, Annales UMCS Informatica, 2004, pp. 183-189.
  • [16] TADEUSIEWICZ R., Elementarne wprowadzenie do sieci neuronowych z przykładowymi programami, Akademicka Oficyna Wydawnicza, Warszawa, 1998, (in Polish).
  • [17] TADEUSIEWICZ R., Sieci neuronowe, Akademicka Oficyna Wydawnicza, Warszawa, 1993, (in Polish).
  • [18] TRAUNMÜLLER H. Analytical expressions for the tonotopic sensory scale, J. Acoust. Soc. Am., Vol. 88, 1990, pp. 97-100.
  • [19] WIŚNIEWSKI M., KUNISZYK-JÓŹKOWIAK W., SMOŁKA E., SUSZYŃSKI W., Improved approach to automatic detection of speech disorders based on the Hidden Markov Models approach, Journal of Medical Informatics & Technologies Vol. 15, 2010, pp. 145-152.
  • [20] KOBUS A., KUNISZYK-JÓŹKOWIAK W., SMOŁKA E., CODELLO I., Speech nonfluency detection and classification based on linear prediction coefficients and neural networks, Journal of Medical Informatics & Technologies Vol. 15, 2010, pp. 135-144.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0016-0013
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