A comparative analysis of Serbian phonemes: Linear and non-linear models/Uporedna analiza fonema srpskog jezika: linearni i nelinearni modeli

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 sy...

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Veröffentlicht in:Vojnotehnički glasnik 2014-10, Vol.62 (4), p.7-37
1. Verfasser: Danijela D. Protić
Format: Artikel
Sprache:eng
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Zusammenfassung: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. / U radu je prikazana analiza karakteristika vokala i nevokala srpskog jezika. Vokale karakteriše kvaziperiodičnost i spektar snage signala sa dobro uočljivim formantima. Nevokale karakteriše kratkotrajna kvaziperiodičnost i mala snaga pobudnog signala. Vokali i nevokali modelovani su linearnim AR modelima i odgovarajućim nelinearnim modelima koji su generisani kao feed-forward neuronska mreža sa jednim skrivenim slojem. U procesu modelovanja korišćena je minimizacija srednje kvadratne greške sa propagacijom unazad, a kriterijum izbora optimalnog modela jeste zaustavljanje obučavanja, kada normalizovana srednja kvadratna test greška ili finalna greška predikcije dostignu minimalnu vrednost. LM metod korišćen je za proračun inverzne Hessianove matrice, a za pruning je upotrebljen Optimal Brain Surgeon. Prikazana su generalizaciona svojstva signala u vremenskom i frekvencijskom domenu, a kroskorelacionom analizom utvrđen je odnos signala na izlazima neurona skrivenog sloja.
ISSN:0042-8469
2217-4753
DOI:10.5937/vojtehg62-5170