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.
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This research is funded by the European Social Fund under the No 09.3.3-LMT-K-712 “Development of Competences of Scientists, other Researchers and Students through Practical Research Activities” measure.
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Korvel, G., Kurasova, O., Kostek, B. (2019). Comparative Analysis of Spectral and Cepstral Feature Extraction Techniques for Phoneme Modelling. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_48
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