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Full-Text Articles in Computational Linguistics
Neural Network Vs. Rule-Based G2p: A Hybrid Approach To Stress Prediction And Related Vowel Reduction In Bulgarian, Maria Karamihaylova
Neural Network Vs. Rule-Based G2p: A Hybrid Approach To Stress Prediction And Related Vowel Reduction In Bulgarian, Maria Karamihaylova
Dissertations, Theses, and Capstone Projects
An effective grapheme-to-phoneme (G2P) conversion system is a critical element of speech synthesis. Rule-based systems were an early method for G2P conversion. In recent years, machine learning tools have been shown to outperform rule-based approaches in G2P tasks. We investigate neural network sequence-to-sequence modeling for the prediction of syllable stress and resulting vowel reductions in the Bulgarian language. We then develop a hybrid G2P approach which combines manually written grapheme-to-phoneme mapping rules with neural network-enabled syllable stress predictions by inserting stress markers in the predicted stress position of the transcription produced by the rule-based finite-state transducer. Finally, we apply vowel …