Transcribing spoken audio samples into the International Phonetic Alphabet (IPA) has long been reserved for experts. In this study, we examine the use of an Artificial Neural Network (ANN) model to automatically extract the IPA phonemic pronunciation of a word based on its audio pronunciation, hence its name Generating IPA Pronunciation From Audio (GIPFA). Based on the French Wikimedia dictionary, we trained our model which then correctly predicted 75% of the IPA pronunciations tested. Interestingly, by studying inference errors, the model made it possible to highlight possible errors in the dataset as well as to identify the closest phonemes in French.
Cite
@article{arxiv.2006.07573,
title = {GIPFA: Generating IPA Pronunciation from Audio},
author = {Xavier Marjou},
journal= {arXiv preprint arXiv:2006.07573},
year = {2021}
}