Grapheme-to-Phoneme Conversion with Convolutional Neural Networks

Massively Multilingual Neural Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems. Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach t...

Hybrid Grapheme to Phoneme Conversion forUnlimited

Both dictionary-based and rule-based methods on grapheme-to-phoneme conversion have their own advantages and limitations. For example, a large sized phonetic dictionary and complex morphophonemic rules are required for the dictionary-based method and the LTS(letter to sound) rule-based method itself cannot model the complete morphophonemic constraints. This paper describes a grapheme-to-phoneme...

Lexical Access via Phoneme to Grapheme Conversion

The Lexical Access (LA) problem in Computer Science aims to match a phoneme sequence produced by the user to a correctly spelled word in a lexicon, with minimal human intervention and in a short amount of time. Lexical Access is useful in the case where the user knows the spoken form of a word but cannot guess its written form or where the users best guess is inappropriate for look-up in a stan...

Unlimited Vocabulary Grapheme to Phoneme Conversion forKorean

This paper describes a grapheme-to-phoneme conversion method using phoneme connectivity and CCV conversion rules. The method consists of mainly four modules including morpheme normalization, phrase-break detection , morpheme to phoneme conversion and phoneme connectivity check. The morpheme normalization is to replace non-Korean symbols into standard Korean graphemes. The phrase-break detector ...

Phoneme to grapheme conversion by rules