Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages
Ngoc Tan Le, Fatiha Sadat · 2018
Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems.With lowresource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful.These models are based on initial alignments between grapheme source and phoneme target sequences.Inspired by sequence-tosequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair.We participated in the NEWS 2018 shared task for the English-Vietnamese transliteration task.