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2014, vol. 62, iss. 4, pp. 7-37
A comparative analysis of Serbian phonemes: Linear and non-linear models
General Staff of the Serbian Army, Department of Telecommunications and Information Technology (J-6), Centre for Applied Mathematics and Electronics, Belgrade
Keywords: AR model; neural networks; speech
This paper presents the results of a comparative analysis of Serbian phonemes. The characteristics of vowels are quasi-periodicity and clearly visible formants. Non-vowels are short-term quasi-periodical signals having a low power excitation signal. For the purpose of this work, speech production systems were modelled with linear AR models and the corresponding non-linear models, based feed-forward neural networks with one hidden-layer. Sum squared error minimization as well as the back-propagation algorithm were used to train models. The selection of the optimal model was based on two stopping criteria: the normalized mean squares test error and the final prediction error. The Levenberg-Marquart method was used for the Hessian matrix calculation. The Optimal Brain Surgeon method was used for pruning. The generalization properties, based on the time-domain and signal spectra of outputs at hidden-layer neurons, are presented.
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article language: English
document type: Original Scientific Paper
DOI: 10.5937/vojtehg62-5170
published in SCIndeks: 22/10/2014
peer review method: double-blind

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