Low-Resource Machine Transliteration Using Recurrent Neural Networks

Abstract

Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource 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-to-sequence recurrent neural network--based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pretrained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. Evaluation and experiments involving French and Vietnamese showed that with only a small bilingual pronunciation dictionary available for training the transliteration models, promising results were obtained with a large increase in BLEU scores and a reduction in Translation Error Rate (TER) and Phoneme Error Rate (PER). Moreover, we compared our proposed neural network--based transliteration approach with a statistical one.