Selective Augmentation: Improving Universal Automatic Phonetic Transcription via G2P Bootstrapping

This research proposes Selective Augmentation, a bootstrapping method to improve universal automatic phonetic transcription (APT) by selectively transferring linguistic distinctions to address limited high-quality training data. Exemplified with the MultIPA model, the approach enhanced plosive voicing accuracy by 17.6% and introduced aspiration recognition using data augmented from a helper language like Hindi.