Bi-directional conversion between graphemes and phonemes using a joint N-gram model

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Sequence-to-sequence neural net models for grapheme-to-phoneme conversion (2015, background): “Joint modeling has been proposed for grapheme-tophoneme conversion [18, 19, 21].”
Conditional and joint models for grapheme-to-phoneme conversion (2003, methodology): “Many joint models have been proposed for grapheme-to-pho-neme conversion ( e.g. , [6, 7]).”
Inequality Maximum Entropy Classifier with Character Features for Polyphone Disambiguation in Mandarin TTS Systems (2007, background): “Although many approaches including finite state transducer (FST) [5] and”
A bi-directional LSTM approach for polyphone disambiguation in Mandarin Chinese (2016, background): “In the G2P conversion task [1, 2, 3, 4, 5, 6, 7], a neural network accepts a sequence of characters and outputs a sequence of pronunciations.”
Joint-sequence models for grapheme-to-phoneme conversion (2008, background): “The method described by Bagshaw (1998) operates with a set of hand-speciﬁed grapheme phoneme correspondences (GPC, cf. Section 3.2) and induces context dependent rules over these units (with a context size of two GPC positions in both directions).”