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Abstract
Phoneme parameter extraction framework based on spectral and cepstral parameters is proposed. Using this framework, the phoneme signal is divided into frames and Hamming window is used. The performances are evaluated for recognition of Lithuanian vowel and semivowel phonemes. Different feature sets without noise as well as at different level of noise are considered. Two classical machine learning methods (Naive Bayes and Support Vector Machine) are used for classifying each problem, separately. The experiment results show that cepstral parameters give higher accuracies than spectral parameters. Moreover, cepstral parameters give better performance compared to spectral parameters in noisy conditions.
Citations
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2
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0
Web of Science
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4
Scopus
Authors (3)
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Grazina Korvel
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Olga Kurasova dr
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Keywords
Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- Multimedia and Network Information Systems strony 480 - 489
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Korvel G., Kurasova O., Kostek B..: Comparative analysis of spectral and cepstral feature extraction techniques for phoneme modelling, W: Multimedia and Network Information Systems, 2018, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-98678-4_48
- Sources of funding:
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- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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