Grapheme-to-phoneme conversion with neural networks: Automatic morphological analysis

Maiia Bikmetova, Korin Richmond

Research output: Contribution to journalArticlepeer-review


The paper provides an alternative approach to one of the key steps of speech synthesis – grapheme-to-phoneme conversion. The approach is based on automatic morphological analysis of out-of-vocabulary words into their consitutent morphemes. In contrast to a traditional method of morphological analysis based on finite state transducers (FST), we propose here a solution that makes use of neural networks. Experiments show that morpho-logical analysis with the proposed neural network approach is significantly more effective(accuracy 93.8%) than than morphological analysis with FST (accuracy 75%). The proposed approach allows to speed up the creation of new grapheme-to-phoneme models, as well as making it easier to build and maintain pronunciation dictionaries.
Translated title of the contributionGrapheme-to-phoneme conversion with neural networks: Automatic morphological analysis
Original languageRussian
Pages (from-to)121-126
Issue number2(84)
Publication statusPublished - 4 Jul 2019


  • grapheme-to-phoneme conversion
  • morphological analysis
  • neural networks

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