Abstract
Abstract
In English phonetic synthesis, it is impossible to create a thesaurus containing all vocabulary as English has an almost unlimited vocabulary. Hence, for English words that are not included in the thesaurus, generating phonetic symbols through the “Grapheme-to-phoneme (G2P)” algorithm is the best solution. For this purpose, a dynamic finite generalization (DFGA) machine learning algorithm for the rules of G2P conversion is proposed in this paper. The dictionary library used for learning has 27,040 words, 90% of which are used for rule learning, and the remaining 10% are used for testing. After ten rounds of cross-validation, the average grapheme conversion accuracy in the learning and test sets is 99.78% and 93.14%, and the average vocabulary conversion accuracy is 99.56% and 73.51%, respectively.
Subject
General Physics and Astronomy
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