Grapheme-to-Phoneme Conversion System

A Grapheme-to-Phoneme Conversion System is a speech generation system that converts graphemes to phonemes.

• AKA: G2P Engine.
• Context:
  • It implements a G2P algorithm to solve a G2P task.
  • It can require a Text-to-Speech Conversion System.
  • …
• Example(s):
  • a LSTM-RNN Grapheme-to-Phoneme Conversion System (Rao et al., 2015),
  • a Conditional Maximum Entropy Grapheme-to-Phoneme Conversion System (Chen, 2003),
  • a Joint Conditional Maximum Entropy N-Gram Grapheme-to-Phoneme Conversion System (Chen, 2003),
  • …
• Counter-Example(s):
  • an Optical Character Recognition System,
  • an Image-to-Text Conversion System,
  • …
• See: Video Recognition System, Dialog System, Language Model, Machine Translation System, Natural Language Processing System.

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References

2015

• (Rao et al., 2015) ⇒ Kanishka Rao, Fuchun Peng, Haşim Sak, and Françoise Beaufays. (2015). “Grapheme-to-phoneme Conversion Using Long Short-term Memory Recurrent Neural Networks.” In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. doi:10.1109/ICASSP.2015.7178767
  • QUOTE: Grapheme-to-phoneme (G2P) models are key components in speech recognition and text-to-speech systems as they describe how words are pronounced.

2003

• (Chen, 2003) ⇒ Stanley F. Chen (2003). "Conditional and Joint Models for Grapheme-to-Phoneme Conversion". In: Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech 2003- Interspeech 2003).
  • QUOTE: In this work, we introduce several models for grapheme-to-phoneme conversion: a conditional maximum entropy model, a joint maximum entropy n-gram model, and a joint maximum entropy n-gram model with syllabification. We examine the relative merits of conditional and joint models for this task, and find that joint models have many advantages. We show that the performance of our best model, the joint n-gram model, compares favorably with the best results for English grapheme-to-phoneme conversion reported in the literature, sometimes by a wide margin.

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