A computational grammar of discourse-neutral prosodic phrasing in English
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic corpus-based tone and break-index prediction using K-ToBI representation
ACM Transactions on Asian Language Information Processing (TALIP)
Stochastic and syntactic techniques for predicting phrase breaks
Computer Speech and Language
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A prosodic phrasing model for a Korean text-to-speech synthesis system
Computer Speech and Language
Prediction of Korean Prosodic Phrase Boundary by Efficient Feature Selection in Machine Learning
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
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This article presents a prosodic phrasing model for a general purpose Korean speech synthesis system. To reflect the factors affecting prosodic phrasing in the model, linguistically motivated machine-learning features were investigated. These features were effectively incorporated using a stacking model. The phrasing performance was also improved through feature engineering. The corpus used in the experiment is a 4,392-sentence corpus (55,015 words with an average of 13 words per sentence). Because the corpus contains speaker-dependent variability and such variability is not appropriately reflected in a general purpose speech synthesis system, a method to reduce such variability is proposed. In addition, the entire set of data used in the experiment is provided to the public for future use in comparative research.