Comparative analysis of various transformation techniques for voiceless consonants modeling

Grazina Korvel; Bożena Kostek; Olga Kurasova

Comparative analysis of various transformation techniques for voiceless consonants modeling

Abstract

In this paper, a comparison of various transformation techniques, namely Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Walsh Hadamard Transform (DWHT) are performed in the context of their application to voiceless consonant modeling. Speech features based on these transformation techniques are extracted. These features are mean and derivative values of cepstrum coefficients, derived from each transformation. Feature extraction is performed on the speech signal divided into short-time segments. The kNN and Naive Bayes methods are used for phoneme classification. We consider both classfication accuracies and computational time. Experiments show that DFT and DCT give better classification accuracy than DWHT. The result of DFT was not significantly different from DCT, but it was for DWHT. The same tendency was revealed for DCT. It was checked with the usage of the ANOVA test that the difference between results obtained by DCT and DWHT is significant.

Authors (3)

Grazina Korvel
- Vilnius University Institute of Data Science and Digital Technologies
Bożena Kostek prof. dr hab. inż.
Olga Kurasova dr
- Vilnius University Institute of Data Science and Digital Technologies

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Keywords

TRANSFORMACJA FOURIERA, DYSKRETNA TRANSFORMACJA KOSINUSOWA, DYSKRETNA TRANSFORMACJA WALSH-HADAMARDA

Details

Category:

Articles

Type:

artykuł w czasopiśmie wyróżnionym w JCR

Published in:

International Journal of Computers Communications & Control no. 13, pages 853 - 864,
ISSN: 1841-9836

Language:

English

Publication year:

2018

Bibliographic description:

Korvel G., Kostek B., Kurasova O.: Comparative analysis of various transformation techniques for voiceless consonants modeling// International Journal of Computers Communications & Control. -Vol. 13, nr. 5 (2018), s.853-864

Sources of funding: